model_name
stringclasses 20
values | predicted_class
stringclasses 4
values | task_name
stringlengths 13
44
| narration
stringlengths 473
1.48k
| values
sequence | sign
sequence | narrative_id
int32 1
454
| unique_id
int32 0
468
| classes_dict
stringlengths 30
63
| narrative_questions
sequence | feature_nums
sequence | ft_num2name
stringlengths 78
3.67k
| old2new_ft_nums
stringlengths 72
1.28k
| old2new_classes
stringclasses 22
values | predicted_class_label
stringlengths 2
23
| class2name
stringlengths 25
85
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SVC | C1 | Health Care Services Satisfaction Prediction | The prediction probability associated with class C2 and class C1, respectively, is 35.34% and 64.66%. Based on these probabilities, the model labels the given case as C1 since it is the most probable class. According to the attribution analysis, the most relevant features considered by the model here are F5, F1, and F8, while the least relevant features are F12, F2, and F4. Regarding the direction of influence of the features, F5, F1, F8, and F7 are the top positively supporting features, driving the decision higher in favour of C1. Further increasing the probability that C1 is the true label are the values of other positive features such as F16, F3, F15, and F14. To explain why the likelihood of C2 is 35.34%, we have to look at the negative contributions from F11, F6, F13, F2, F12, and F4. The abovementioned negative features contradict the model's decision with respect to the classification outcome. | [
"0.05",
"0.03",
"0.03",
"-0.03",
"0.02",
"-0.02",
"0.02",
"0.02",
"-0.02",
"0.02",
"0.01",
"0.01",
"0.00",
"-0.00",
"-0.00",
"-0.00"
] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative"
] | 208 | 445 | {'C2': '35.34%', 'C1': '64.66%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F6, F16 and F15) with moderate impact on the prediction made for this test case."
] | [
"F5",
"F8",
"F1",
"F11",
"F7",
"F6",
"F16",
"F15",
"F13",
"F3",
"F14",
"F9",
"F10",
"F2",
"F12",
"F4"
] | {'F5': 'waiting rooms', 'F8': 'Hygiene and cleaning', 'F1': 'Specialists avaliable', 'F11': 'Quality\\/experience dr.', 'F7': 'Modern equipment', 'F6': 'Exact diagnosis', 'F16': 'hospital rooms quality', 'F15': 'Check up appointment', 'F13': 'avaliablity of drugs', 'F3': 'friendly health care workers', 'F14': 'Time waiting', 'F9': 'Communication with dr', 'F10': 'lab services', 'F2': 'parking, playing rooms, caffes', 'F12': 'Time of appointment', 'F4': 'Admin procedures'} | {'F14': 'F5', 'F4': 'F8', 'F7': 'F1', 'F6': 'F11', 'F10': 'F7', 'F9': 'F6', 'F15': 'F16', 'F1': 'F15', 'F13': 'F13', 'F11': 'F3', 'F2': 'F14', 'F8': 'F9', 'F12': 'F10', 'F16': 'F2', 'F5': 'F12', 'F3': 'F4'} | {'C1': 'C2', 'C2': 'C1'} | Satisfied | {'C2': 'Dissatisfied', 'C1': 'Satisfied'} |
KNeighborsClassifier | C2 | Real Estate Investment | The classifier is very uncertain about the correct label for the case given. Regarding the classifier's decision, there is close to an even split on the probability of either of the possible labels is the correct label but the classifier chooses the label as C2. The prediction verdict above is attributed to the contributions of mainly the following features: F4, F11, F3, and F15, however, the lowest ranked features are F7, F19, and F1. Analysing the direction of influence of the features shows that there are ten positive and ten negative features. Positive features such as F3, F15, F8, and F20 increase the response of the classifier in favour of the assigned label. Conversely, negative features such as F4, F11, F12, and F2 decrease the likelihood of C2 being the correct label given that their values support the alternative label, C1. The uncertainty concerning the label assignment can be due to the fact that the top negative features F4 and F11 have very high attributions shifting the classifier's verdict away from the C2 class. | [
"-0.32",
"-0.24",
"0.04",
"0.03",
"0.02",
"-0.02",
"-0.02",
"0.02",
"-0.02",
"-0.02",
"0.02",
"0.01",
"-0.01",
"-0.01",
"0.01",
"-0.00",
"-0.00",
"0.00",
"0.00",
"0.00"
] | [
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive"
] | 185 | 107 | {'C2': '50.00%', 'C1': '50.00%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F4 and F11.",
"Summarize the direction of influence of the features (F3, F15, F8 and F12) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F4",
"F11",
"F3",
"F15",
"F8",
"F12",
"F2",
"F20",
"F10",
"F9",
"F14",
"F16",
"F18",
"F6",
"F5",
"F17",
"F13",
"F7",
"F19",
"F1"
] | {'F4': 'Feature7', 'F11': 'Feature4', 'F3': 'Feature2', 'F15': 'Feature8', 'F8': 'Feature20', 'F12': 'Feature1', 'F2': 'Feature12', 'F20': 'Feature15', 'F10': 'Feature6', 'F9': 'Feature9', 'F14': 'Feature17', 'F16': 'Feature3', 'F18': 'Feature19', 'F6': 'Feature13', 'F5': 'Feature18', 'F17': 'Feature5', 'F13': 'Feature11', 'F7': 'Feature16', 'F19': 'Feature10', 'F1': 'Feature14'} | {'F11': 'F4', 'F9': 'F11', 'F1': 'F3', 'F3': 'F15', 'F20': 'F8', 'F7': 'F12', 'F15': 'F2', 'F4': 'F20', 'F10': 'F10', 'F12': 'F9', 'F6': 'F14', 'F8': 'F16', 'F5': 'F18', 'F16': 'F6', 'F19': 'F5', 'F2': 'F17', 'F14': 'F13', 'F18': 'F7', 'F13': 'F19', 'F17': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Ignore | {'C2': 'Ignore', 'C1': 'Invest'} |
DecisionTreeClassifier | C2 | Car Acceptability Valuation | The classification algorithm believes that C2 is the output label that was generated with 100% certainty and that C1 is unlikely to be the correct label in this case. According to the attribution investigations, the following input features are ranked from most relevant to least relevant: F2, F4, F6, F1, F3, and F5. As shown by the attribution plot, F2 is the only one shown to positively contribute to the above classification decision, while the others contribute negatively. The contributions of negative features such as F4, F6, and F1 result in the decision being driven in a different direction. From the prediction confidence level, we can conclude that the very strong influence of F2 overshadows the contributions of the negative features hence the very high confidence level. | [
"0.42",
"-0.24",
"-0.11",
"-0.09",
"-0.05",
"-0.04"
] | [
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 18 | 309 | {'C2': '100.00%', 'C1': '0.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: ?"
] | [
"F2",
"F4",
"F6",
"F1",
"F3",
"F5"
] | {'F2': 'safety', 'F4': 'persons', 'F6': 'buying', 'F1': 'maint', 'F3': 'lug_boot', 'F5': 'doors'} | {'F6': 'F2', 'F4': 'F4', 'F1': 'F6', 'F2': 'F1', 'F5': 'F3', 'F3': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Unacceptable | {'C2': 'Unacceptable', 'C1': 'Acceptable'} |
RandomForestClassifier | C3 | Flight Price-Range Classification | Of the three possible labels, there is 100.0% confidence that C3 is the most probable label for the given case. The features that heavily influence the classification verdict presented here are F5, F10, and F6, and they have a very strong positive contribution, increasing the odds of the C3 prediction. Other features with a positive influence on the model are F3, F1, F12, F7, and F4. On the contrary, F9, F2, and F8 make the model's decision fluctuate negatively towards selecting an alternative label. All of the negative features mentioned above have a low to moderate impact on the classification verdict presented here compared to F6, F10, and F5. Finally, F11 with its very low positive impact is the least ranked feature marginally pushing the decision towards the assigned label. | [
"0.23",
"0.19",
"0.17",
"0.06",
"-0.06",
"0.05",
"0.04",
"0.01",
"0.01",
"-0.01",
"-0.01",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive"
] | 114 | 237 | {'C3': '100.00%', 'C2': '0.00%', 'C1': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F10 (value equal to V4), F1, F9 (when it is equal to V0) and F3 (when it is equal to V2)) with moderate impact on the prediction made for this test case."
] | [
"F6",
"F5",
"F10",
"F1",
"F9",
"F3",
"F4",
"F7",
"F12",
"F2",
"F8",
"F11"
] | {'F6': 'Duration_hours', 'F5': 'Airline', 'F10': 'Total_Stops', 'F1': 'Journey_day', 'F9': 'Source', 'F3': 'Destination', 'F4': 'Journey_month', 'F7': 'Dep_minute', 'F12': 'Arrival_minute', 'F2': 'Arrival_hour', 'F8': 'Duration_mins', 'F11': 'Dep_hour'} | {'F7': 'F6', 'F9': 'F5', 'F12': 'F10', 'F1': 'F1', 'F10': 'F9', 'F11': 'F3', 'F2': 'F4', 'F4': 'F7', 'F6': 'F12', 'F5': 'F2', 'F8': 'F8', 'F3': 'F11'} | {'C1': 'C3', 'C2': 'C2', 'C3': 'C1'} | Low | {'C3': 'Low', 'C2': 'Moderate', 'C1': 'High'} |
RandomForestClassifier | C1 | Used Cars Price-Range Prediction | Per the model, class C2 has a prediction probability of 10.50 percent, whereas class C1 has a predicted probability of 89.50 percent. As a result of the model, it can be determined that C1 is the most likely label for the given scenario. All of the input features are shown to contribute to the above conclusion, with F1, F7, and F3 having the most influence on the classification decision. The least influential features with regard to this classification are F5, F2, F10, and F4, whereas, the impact of F8, F6, and F9 can be classified as modest. The large positive contributions of F7 and F1 are responsible for the model's high confidence which further supported by the positive contributions of F8, F5, and F2. In conclusion, the negative features F3, F6, F10, F9, and F4 favour labelling the case as C2 hence the associated predicted probability. | [
"0.24",
"0.23",
"-0.14",
"0.12",
"-0.10",
"-0.03",
"0.01",
"-0.01",
"0.01",
"-0.00"
] | [
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 259 | 334 | {'C2': '10.50%', 'C1': '89.50%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F9, F5 and F10) with moderate impact on the prediction made for this test case."
] | [
"F7",
"F1",
"F3",
"F8",
"F6",
"F9",
"F5",
"F10",
"F2",
"F4"
] | {'F7': 'Power', 'F1': 'car_age', 'F3': 'Transmission', 'F8': 'Fuel_Type', 'F6': 'Name', 'F9': 'Mileage', 'F5': 'Engine', 'F10': 'Owner_Type', 'F2': 'Kilometers_Driven', 'F4': 'Seats'} | {'F4': 'F7', 'F5': 'F1', 'F8': 'F3', 'F7': 'F8', 'F6': 'F6', 'F2': 'F9', 'F3': 'F5', 'F9': 'F10', 'F1': 'F2', 'F10': 'F4'} | {'C1': 'C2', 'C2': 'C1'} | High | {'C2': 'Low', 'C1': 'High'} |
AdaBoostClassifier | C2 | Basketball Players Career Length Prediction | The classifier says that C2 is the most likely label for the provided data with relatively high confidence. It is crucial to remember, however, that there is a 21.80% possibility that it is C1. F16 and F19 are the major driving variables for the aforementioned classification or prediction choice. The remaining variables F1, F12, F17, and F6 have a modest to minor impact on the selection made above. Among the input variables, F1, F6, F4, F9, and F10 are the subset that have a negative influence or contribution whereas all of the remaining variables have a positive impact. In essence, the substantial positive contributions of F16 and F19, together with the contributions of additional positive variables such as F12, F17, F8, and F7, account for the classifier's confidence in this classification. | [
"0.08",
"0.06",
"-0.00",
"0.00",
"0.00",
"-0.00",
"0.00",
"0.00",
"-0.00",
"0.00",
"-0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"-0.00",
"0.00"
] | [
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive"
] | 256 | 337 | {'C2': '78.20%', 'C1': '21.80%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F8, F7 and F4?"
] | [
"F16",
"F19",
"F1",
"F12",
"F17",
"F6",
"F8",
"F7",
"F4",
"F15",
"F9",
"F11",
"F5",
"F18",
"F14",
"F3",
"F13",
"F10",
"F2"
] | {'F16': 'GamesPlayed', 'F19': 'PointsPerGame', 'F1': 'Steals', 'F12': 'MinutesPlayed', 'F17': 'DefensiveRebounds', 'F6': 'Rebounds', 'F8': 'Blocks', 'F7': 'FreeThrowAttempt', 'F4': 'FieldGoalPercent', 'F15': 'FreeThrowMade', 'F9': 'OffensiveRebounds', 'F11': 'FieldGoalsMade', 'F5': '3PointAttempt', 'F18': 'FreeThrowPercent', 'F14': '3PointMade', 'F3': 'FieldGoalsAttempt', 'F13': 'Turnovers', 'F10': 'Assists', 'F2': '3PointPercent'} | {'F1': 'F16', 'F3': 'F19', 'F17': 'F1', 'F2': 'F12', 'F14': 'F17', 'F15': 'F6', 'F18': 'F8', 'F11': 'F7', 'F6': 'F4', 'F10': 'F15', 'F13': 'F9', 'F4': 'F11', 'F8': 'F5', 'F12': 'F18', 'F7': 'F14', 'F5': 'F3', 'F19': 'F13', 'F16': 'F10', 'F9': 'F2'} | {'C1': 'C2', 'C2': 'C1'} | More than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
RandomForestClassifier | C1 | Printer Sales | C1 has an 83.0% chance of being the correct label for the case under consideration, making C2 the least likely class with a predicted likelihood of 17.0%. F24, F14, and F16 features have a significant impact on class selection here while on the other hand, the remaining features are shown to have marginal to no contribution to the classification verdict here. In actual fact, the values for F3, F9, F7, F4, F12, and F23 may have been ignored by the classifier because their respective influences are almost zero. Of the important features, only F26, F11, F22, F20, F6, and F19 are negative and this is mainly because their contribution to selection tends to reduce the chance that C1 is the correct label, preferring that the case is classified as C2. The remaining features such as F24, F14, F16, F21, F1, and F13 strongly contribute positively, increasing the chances of C1 which explains the level of certainty associated with C1. | [
"0.10",
"0.07",
"0.06",
"0.06",
"0.03",
"0.03",
"-0.02",
"0.02",
"0.02",
"-0.02",
"-0.02",
"0.02",
"0.02",
"0.01",
"-0.01",
"-0.01",
"-0.01",
"0.01",
"0.01",
"0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 240 | 323 | {'C1': '83.00%', 'C2': '17.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F26, F25 and F5?"
] | [
"F24",
"F14",
"F16",
"F1",
"F21",
"F13",
"F26",
"F25",
"F5",
"F11",
"F22",
"F18",
"F2",
"F10",
"F20",
"F6",
"F19",
"F17",
"F8",
"F15",
"F3",
"F9",
"F7",
"F4",
"F12",
"F23"
] | {'F24': 'X8', 'F14': 'X24', 'F16': 'X1', 'F1': 'X2', 'F21': 'X10', 'F13': 'X15', 'F26': 'X25', 'F25': 'X23', 'F5': 'X18', 'F11': 'X4', 'F22': 'X7', 'F18': 'X17', 'F2': 'X3', 'F10': 'X22', 'F20': 'X5', 'F6': 'X9', 'F19': 'X12', 'F17': 'X19', 'F8': 'X11', 'F15': 'X16', 'F3': 'X14', 'F9': 'X21', 'F7': 'X20', 'F4': 'X13', 'F12': 'X6', 'F23': 'X26'} | {'F8': 'F24', 'F24': 'F14', 'F1': 'F16', 'F2': 'F1', 'F10': 'F21', 'F15': 'F13', 'F25': 'F26', 'F23': 'F25', 'F18': 'F5', 'F4': 'F11', 'F7': 'F22', 'F17': 'F18', 'F3': 'F2', 'F22': 'F10', 'F5': 'F20', 'F9': 'F6', 'F12': 'F19', 'F19': 'F17', 'F11': 'F8', 'F16': 'F15', 'F14': 'F3', 'F21': 'F9', 'F20': 'F7', 'F13': 'F4', 'F6': 'F12', 'F26': 'F23'} | {'C1': 'C1', 'C2': 'C2'} | Less | {'C1': 'Less', 'C2': 'More'} |
LogisticRegression | C2 | Music Concert Attendance | C2 is the label picked by the algorithm with about 82.06% certainty, since the prediction likelihood of C1 is only 17.94%. F20, F5, F10, and F3 all contribute significantly to the above classification output and among them, the features that support the most positive contribution to the C2 prediction are F3, F20, and F5, while F10 drives the final prediction against assigning C2 in support of C1. F1 also contributes positively to the classification here, but F18 contributes negatively and like F10 favours C1. Finally, according to the analysis, F11, F13, F7, and F8 all have little effect on the final prediction made by the algorithm for this case. | [
"0.29",
"0.27",
"-0.22",
"0.13",
"-0.06",
"0.04",
"0.04",
"-0.04",
"0.04",
"-0.03",
"-0.03",
"0.03",
"-0.03",
"0.02",
"0.02",
"-0.02",
"0.02",
"0.01",
"-0.00",
"0.00"
] | [
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive"
] | 46 | 295 | {'C1': '17.94%', 'C2': '82.06%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F5, F18 and F1) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F3",
"F20",
"F10",
"F5",
"F18",
"F1",
"F17",
"F4",
"F15",
"F14",
"F19",
"F9",
"F16",
"F12",
"F6",
"F2",
"F7",
"F11",
"F8",
"F13"
] | {'F3': 'X11', 'F20': 'X1', 'F10': 'X13', 'F5': 'X3', 'F18': 'X8', 'F1': 'X6', 'F17': 'X2', 'F4': 'X9', 'F15': 'X17', 'F14': 'X10', 'F19': 'X4', 'F9': 'X14', 'F16': 'X20', 'F12': 'X18', 'F6': 'X19', 'F2': 'X7', 'F7': 'X12', 'F11': 'X15', 'F8': 'X16', 'F13': 'X5'} | {'F11': 'F3', 'F1': 'F20', 'F13': 'F10', 'F3': 'F5', 'F8': 'F18', 'F6': 'F1', 'F2': 'F17', 'F9': 'F4', 'F17': 'F15', 'F10': 'F14', 'F4': 'F19', 'F14': 'F9', 'F20': 'F16', 'F18': 'F12', 'F19': 'F6', 'F7': 'F2', 'F12': 'F7', 'F15': 'F11', 'F16': 'F8', 'F5': 'F13'} | {'C1': 'C1', 'C2': 'C2'} | > 10k | {'C1': '< 10k', 'C2': '> 10k'} |
LogisticRegression | C1 | Flight Price-Range Classification | The model is confident in its prediction, as it predicted class C1 with a likelihood of 90.48% and hence, for the given case, there is a smaller chance of it being any other class label. F6 and F10 are deemed the most important features whereas on the other hand all the other features have moderate to minimal amounts of influence. Both F6 and F10 have the same direction of impact, increasing the odds of the predicted label, C1. While F4 and F1 are both encouraging the model to make a prediction of C1, the others F9, F12, and F8 is pushing the model towards a different label. Many features have moderately low impact on the final prediction, but the features F3, F8, and F2 are those with the smallest influence. | [
"0.40",
"0.35",
"0.11",
"0.05",
"-0.04",
"0.03",
"0.03",
"-0.02",
"0.02",
"0.02",
"-0.01",
"-0.01"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 89 | 37 | {'C1': '90.48%', 'C3': '9.51%', 'C2': '0.01%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F6 (equal to V4) and F10 (equal to V3).",
"Summarize the direction of influence of the features (F4 (equal to V2), F1, F9 (when it is equal to V0) and F11) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F6",
"F10",
"F4",
"F1",
"F9",
"F11",
"F5",
"F12",
"F7",
"F3",
"F8",
"F2"
] | {'F6': 'Total_Stops', 'F10': 'Airline', 'F4': 'Destination', 'F1': 'Arrival_hour', 'F9': 'Source', 'F11': 'Duration_hours', 'F5': 'Dep_hour', 'F12': 'Dep_minute', 'F7': 'Arrival_minute', 'F3': 'Journey_month', 'F8': 'Journey_day', 'F2': 'Duration_mins'} | {'F12': 'F6', 'F9': 'F10', 'F11': 'F4', 'F5': 'F1', 'F10': 'F9', 'F7': 'F11', 'F3': 'F5', 'F4': 'F12', 'F6': 'F7', 'F2': 'F3', 'F1': 'F8', 'F8': 'F2'} | {'C1': 'C1', 'C2': 'C3', 'C3': 'C2'} | Low | {'C1': 'Low', 'C3': 'Moderate', 'C2': 'High'} |
DecisionTreeClassifier | C2 | Airline Passenger Satisfaction | Based on the probability distribution across the classes, the classifier is shown to have a moderately high confidence level in the C2 label assignment, with its likelihood equal to 65.0%, whereas that of C1 is only 35.0%. The prediction decision above is predominantly due to the influence of the variables F22, F12, F23, and F9. On the lower end are the least relevant variables, F11, F18, F16, F19, F2, and F4, with little to no influence on the classifier when assigning a label to the given instance. On the one hand, the top positive variables are F22, F12, and F23, increasing the probability that C2 is the correct label. Also, the top negative variables are F9, F26, F21, and F3, decreasing the classifier's response and consequently shifting the prediction verdict in the opposite direction towards C1. Other variables with a positive direction of influence are F5, F1, F8, F10, F17, F20, F24, F6, and F7. | [
"0.13",
"0.10",
"0.08",
"-0.06",
"0.04",
"-0.03",
"-0.03",
"-0.03",
"0.02",
"-0.02",
"-0.02",
"-0.02",
"0.02",
"0.01",
"0.01",
"0.01",
"-0.01",
"0.01",
"0.01",
"0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 113 | 468 | {'C1': '35.00%', 'C2': '65.00%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F10 (equal to V2), F12 (equal to V1), F2 (with a value equal to V0) and F21 (value equal to V3)) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F22",
"F12",
"F23",
"F9",
"F5",
"F26",
"F21",
"F3",
"F1",
"F13",
"F15",
"F25",
"F8",
"F10",
"F17",
"F20",
"F14",
"F24",
"F6",
"F7",
"F11",
"F18",
"F16",
"F19",
"F2",
"F4"
] | {'F22': 'X8', 'F12': 'X2', 'F23': 'X1', 'F9': 'X21', 'F5': 'X25', 'F26': 'X10', 'F21': 'X3', 'F3': 'X9', 'F1': 'X15', 'F13': 'X7', 'F15': 'X20', 'F25': 'X12', 'F8': 'X24', 'F10': 'X6', 'F17': 'X17', 'F20': 'X23', 'F14': 'X11', 'F24': 'X22', 'F6': 'X4', 'F7': 'X14', 'F11': 'X19', 'F18': 'X18', 'F16': 'X16', 'F19': 'X13', 'F2': 'X5', 'F4': 'X26'} | {'F8': 'F22', 'F2': 'F12', 'F1': 'F23', 'F21': 'F9', 'F25': 'F5', 'F10': 'F26', 'F3': 'F21', 'F9': 'F3', 'F15': 'F1', 'F7': 'F13', 'F20': 'F15', 'F12': 'F25', 'F24': 'F8', 'F6': 'F10', 'F17': 'F17', 'F23': 'F20', 'F11': 'F14', 'F22': 'F24', 'F4': 'F6', 'F14': 'F7', 'F19': 'F11', 'F18': 'F18', 'F16': 'F16', 'F13': 'F19', 'F5': 'F2', 'F26': 'F4'} | {'C1': 'C1', 'C2': 'C2'} | Acceptable | {'C1': 'neutral or dissatisfied', 'C2': 'satisfied'} |
KNeighborsClassifier | C1 | Company Bankruptcy Prediction | The model's output labelling judgement for the case under consideration is as follows: C2 cannot be the label for the given case; C1 is the most likely class label with a 100.0% confidence level. The key driving factors resulting in the aforementioned classification are the values of the input features: F38, F51, F13, F46, F28, F71, and F44. F70, F61, F85, F20, F59, F93, F14, F66, F24, F89, F30, F65, and F54 are the features that have a modest effect on the decision. Aside from the aforementioned input features, all others, such as F47, F10, F7, and F43, are revealed to be irrelevant to the conclusion reached here. Not all of the influential features support labelling the current instance as C1, and they are referred to as negative features. F44, F61, F30, F65, and F54 are the negative attributes that diminish the likelihood that C1 is the correct label in this case. F38, F51, F13, and F46 are important positive features that strongly increase the likelihood that C1 is the correct label. | [
"0.03",
"0.02",
"0.02",
"0.02",
"0.02",
"-0.02",
"0.02",
"0.02",
"-0.02",
"0.01",
"0.01",
"0.01",
"0.01",
"0.01",
"0.01",
"0.01",
"0.01",
"-0.01",
"-0.01",
"-0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 423 | 352 | {'C1': '100.00%', 'C2': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F28, F44 and F71) with moderate impact on the prediction made for this test case."
] | [
"F38",
"F51",
"F13",
"F46",
"F28",
"F44",
"F71",
"F70",
"F61",
"F85",
"F59",
"F20",
"F93",
"F14",
"F66",
"F24",
"F89",
"F30",
"F65",
"F54",
"F47",
"F10",
"F7",
"F43",
"F87",
"F42",
"F83",
"F17",
"F41",
"F40",
"F23",
"F18",
"F1",
"F34",
"F32",
"F77",
"F67",
"F52",
"F6",
"F64",
"F73",
"F12",
"F9",
"F69",
"F37",
"F35",
"F84",
"F19",
"F36",
"F45",
"F15",
"F81",
"F76",
"F22",
"F57",
"F21",
"F63",
"F53",
"F58",
"F92",
"F88",
"F50",
"F26",
"F62",
"F56",
"F49",
"F8",
"F39",
"F74",
"F86",
"F72",
"F25",
"F68",
"F2",
"F91",
"F80",
"F78",
"F3",
"F60",
"F4",
"F5",
"F75",
"F82",
"F90",
"F27",
"F55",
"F11",
"F48",
"F16",
"F29",
"F33",
"F79",
"F31"
] | {'F38': ' Interest Coverage Ratio (Interest expense to EBIT)', 'F51': ' Net Income to Total Assets', 'F13': ' Realized Sales Gross Profit Growth Rate', 'F46': ' Accounts Receivable Turnover', 'F28': ' Operating Expense Rate', 'F44': ' Contingent liabilities\\/Net worth', 'F71': ' Non-industry income and expenditure\\/revenue', 'F70': ' Current Ratio', 'F61': ' Cash Flow to Liability', 'F85': ' Fixed Assets Turnover Frequency', 'F59': ' Regular Net Profit Growth Rate', 'F20': ' Quick Asset Turnover Rate', 'F93': ' Net Value Per Share (C)', 'F14': ' Operating Profit Growth Rate', 'F66': ' After-tax Net Profit Growth Rate', 'F24': ' Continuous Net Profit Growth Rate', 'F89': ' Net Value Per Share (B)', 'F30': ' Equity to Long-term Liability', 'F65': ' CFO to Assets', 'F54': ' Total debt\\/Total net worth', 'F47': ' Current Asset Turnover Rate', 'F10': " Net Income to Stockholder's Equity", 'F7': ' Operating Gross Margin', 'F43': ' Operating Profit Per Share (Yuan ¥)', 'F87': ' Operating Profit Rate', 'F42': ' Cash Flow Per Share', 'F83': ' Total income\\/Total expense', 'F17': ' No-credit Interval', 'F41': ' Liability to Equity', 'F40': ' Working Capital to Total Assets', 'F23': ' Working Capital\\/Equity', 'F18': ' Long-term Liability to Current Assets', 'F1': ' Interest-bearing debt interest rate', 'F34': ' Inventory and accounts receivable\\/Net value', 'F32': ' Realized Sales Gross Margin', 'F77': ' Current Liability to Equity', 'F67': ' Equity to Liability', 'F52': ' Current Liability to Liability', 'F6': ' Operating profit\\/Paid-in capital', 'F64': ' Operating Funds to Liability', 'F73': ' Current Liability to Current Assets', 'F12': ' Net worth\\/Assets', 'F9': ' Tax rate (A)', 'F69': ' Quick Assets\\/Current Liability', 'F37': ' After-tax net Interest Rate', 'F35': ' Per Share Net profit before tax (Yuan ¥)', 'F84': ' Total Asset Turnover', 'F19': ' Cash Reinvestment %', 'F36': ' Fixed Assets to Assets', 'F45': ' Working capitcal Turnover Rate', 'F15': ' Net profit before tax\\/Paid-in capital', 'F81': ' Net Worth Turnover Rate (times)', 'F76': ' Debt ratio %', 'F22': ' Cash Flow to Equity', 'F57': ' Long-term fund suitability ratio (A)', 'F21': ' Cash Flow to Sales', 'F63': ' Total Asset Growth Rate', 'F53': ' Inventory\\/Current Liability', 'F58': ' Allocation rate per person', 'F92': ' Inventory Turnover Rate (times)', 'F88': ' Operating profit per person', 'F50': ' Net Value Growth Rate', 'F26': ' Interest Expense Ratio', 'F62': ' ROA(B) before interest and depreciation after tax', 'F56': ' Continuous interest rate (after tax)', 'F49': ' Inventory\\/Working Capital', 'F8': ' Retained Earnings to Total Assets', 'F39': ' Total assets to GNP price', 'F74': ' Persistent EPS in the Last Four Seasons', 'F86': ' Quick Ratio', 'F72': ' Revenue per person', 'F25': ' Borrowing dependency', 'F68': ' Cash\\/Total Assets', 'F2': ' ROA(A) before interest and % after tax', 'F91': ' ROA(C) before interest and depreciation before interest', 'F80': ' Average Collection Days', 'F78': ' Current Liabilities\\/Liability', 'F3': ' Cash Flow to Total Assets', 'F60': ' Pre-tax net Interest Rate', 'F4': ' Current Liability to Assets', 'F5': ' Quick Assets\\/Total Assets', 'F75': ' Total expense\\/Assets', 'F82': ' Net Value Per Share (A)', 'F90': ' Current Assets\\/Total Assets', 'F27': ' Research and development expense rate', 'F55': ' Current Liabilities\\/Equity', 'F11': ' Cash flow rate', 'F48': ' Total Asset Return Growth Rate Ratio', 'F16': ' Degree of Financial Leverage (DFL)', 'F29': ' Cash Turnover Rate', 'F33': ' Cash\\/Current Liability', 'F79': ' Revenue Per Share (Yuan ¥)', 'F31': ' Gross Profit to Sales'} | {'F60': 'F38', 'F16': 'F51', 'F38': 'F13', 'F2': 'F46', 'F19': 'F28', 'F64': 'F44', 'F4': 'F71', 'F82': 'F70', 'F50': 'F61', 'F22': 'F85', 'F85': 'F59', 'F33': 'F20', 'F88': 'F93', 'F43': 'F14', 'F80': 'F66', 'F54': 'F24', 'F27': 'F89', 'F23': 'F30', 'F76': 'F65', 'F7': 'F54', 'F61': 'F47', 'F59': 'F10', 'F62': 'F7', 'F63': 'F43', 'F58': 'F87', 'F65': 'F42', 'F57': 'F83', 'F56': 'F17', 'F66': 'F41', 'F67': 'F40', 'F68': 'F23', 'F69': 'F18', 'F1': 'F1', 'F70': 'F34', 'F83': 'F32', 'F92': 'F77', 'F91': 'F67', 'F90': 'F52', 'F89': 'F6', 'F87': 'F64', 'F86': 'F73', 'F84': 'F12', 'F81': 'F9', 'F71': 'F69', 'F79': 'F37', 'F78': 'F35', 'F77': 'F84', 'F75': 'F19', 'F74': 'F36', 'F73': 'F45', 'F72': 'F15', 'F55': 'F81', 'F47': 'F76', 'F53': 'F22', 'F52': 'F57', 'F25': 'F21', 'F24': 'F63', 'F21': 'F53', 'F20': 'F58', 'F18': 'F92', 'F17': 'F88', 'F15': 'F50', 'F14': 'F26', 'F13': 'F62', 'F12': 'F56', 'F11': 'F49', 'F10': 'F8', 'F9': 'F39', 'F8': 'F74', 'F6': 'F86', 'F5': 'F72', 'F3': 'F25', 'F26': 'F68', 'F28': 'F2', 'F29': 'F91', 'F41': 'F80', 'F51': 'F78', 'F49': 'F3', 'F48': 'F60', 'F46': 'F4', 'F45': 'F5', 'F44': 'F75', 'F42': 'F82', 'F40': 'F90', 'F30': 'F27', 'F39': 'F55', 'F37': 'F11', 'F36': 'F48', 'F35': 'F16', 'F34': 'F29', 'F32': 'F33', 'F31': 'F79', 'F93': 'F31'} | {'C1': 'C1', 'C2': 'C2'} | No | {'C1': 'No', 'C2': 'Yes'} |
BernoulliNB | C1 | Student Job Placement | For the case under consideration, the model assigned C1 with very high confidence, since the likelihood of C2 being the right label is only 0.52% which is very small. F11, F7, F3, and F10 have a large positive impact on the model's output prediction. F3 and F10 have a moderately positive impact on the prediction of C1, while F2 has a similar impact but in the opposite direction. F5, F12, and F4 have a very low impact on classification. F6, F8, F1, and F9 have a larger but still insignificant effect. Examining the attributions indicates that there are only two features, F2 and F12, with values that contradict the prediction made here but, their impact on the model is smaller when compared to positive features such as F7, F3, and F11, which explains why the confidence level associated with this classification is high. | [
"0.33",
"0.31",
"0.21",
"0.15",
"-0.13",
"0.08",
"0.06",
"0.04",
"0.03",
"-0.01",
"0.01",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive"
] | 21 | 8 | {'C2': '0.52%', 'C1': '99.48%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F3, F10, F2 and F6 (equal to V1)) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F11",
"F7",
"F3",
"F10",
"F2",
"F6",
"F9",
"F1",
"F8",
"F12",
"F4",
"F5"
] | {'F11': 'workex', 'F7': 'specialisation', 'F3': 'ssc_p', 'F10': 'hsc_p', 'F2': 'degree_p', 'F6': 'gender', 'F9': 'degree_t', 'F1': 'etest_p', 'F8': 'hsc_b', 'F12': 'hsc_s', 'F4': 'ssc_b', 'F5': 'mba_p'} | {'F11': 'F11', 'F12': 'F7', 'F1': 'F3', 'F2': 'F10', 'F3': 'F2', 'F6': 'F6', 'F10': 'F9', 'F4': 'F1', 'F8': 'F8', 'F9': 'F12', 'F7': 'F4', 'F5': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Placed | {'C2': 'Not Placed', 'C1': 'Placed'} |
LogisticRegression | C3 | Flight Price-Range Classification | Since the likelihood of C3 being the true label is shown by the prediction algorithm outputs to be equal to 93.02 percent, there is only a small chance that the true label for the given data instance is any of the other class labels, C2 and C1. The features F12, F1, F11, and F10 are the most important ones driving the label assignment verdict above, and on the other hand, the least relevant features are shown to be F9, F6, and F3. Considering the direction of influence of each input feature, as shown by the attribution analysis, it can be concluded that the positive features steering the prediction higher towards C3 are F12, F1, F10, F11, F2, F4, and F6. The marginal doubt in the predicted output decision is attributed to the negative contributions of F8, F7, F3, F9, and F5. Considering the attributions of the features and predicted probabilities across the classes, it can be concluded that the joint positive contribution outranks the negative contributions; hence, the algorithm is confident that C3 is likely the true label. | [
"0.41",
"0.38",
"0.12",
"0.07",
"-0.06",
"-0.02",
"0.02",
"-0.01",
"0.01",
"-0.00",
"0.00",
"-0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 318 | 418 | {'C3': '93.02%', 'C2': '6.97%', 'C1': '0.01%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F11, F8 and F7) with moderate impact on the prediction made for this test case."
] | [
"F12",
"F1",
"F10",
"F11",
"F8",
"F7",
"F2",
"F5",
"F4",
"F9",
"F6",
"F3"
] | {'F12': 'Total_Stops', 'F1': 'Airline', 'F10': 'Destination', 'F11': 'Journey_day', 'F8': 'Source', 'F7': 'Dep_hour', 'F2': 'Duration_hours', 'F5': 'Dep_minute', 'F4': 'Duration_mins', 'F9': 'Arrival_minute', 'F6': 'Arrival_hour', 'F3': 'Journey_month'} | {'F12': 'F12', 'F9': 'F1', 'F11': 'F10', 'F1': 'F11', 'F10': 'F8', 'F3': 'F7', 'F7': 'F2', 'F4': 'F5', 'F8': 'F4', 'F6': 'F9', 'F5': 'F6', 'F2': 'F3'} | {'C1': 'C3', 'C2': 'C2', 'C3': 'C1'} | Low | {'C3': 'Low', 'C2': 'Moderate', 'C1': 'High'} |
RandomForestClassifier | C1 | Cab Surge Pricing System | Between the three possible classes, there is an 88.0% probability that the correct label for this case is C1. This means that there is a 12.0% chance that the label could be one of the other possible labels, C2 or C3. Increasing the odds of the predicted label are the variables F7, F5, F2, and F10. The next set of variables, F6, F1, and F9, have values that moderately decrease the likelihood of C1 being the correct label. F11, F4, and F3 are the other negatively contributing features, and given that they are lowly ranked, they have a marginal impact when determining the correct label for this case. The other positive features further increasing the probability that C1 is the right label are F12 and F8. Overall, we can conclude that the decision to label the case as C1 is largely due to the strong positive influence of F5, F7, F10, and F2. | [
"0.27",
"0.05",
"0.05",
"0.04",
"-0.03",
"-0.03",
"-0.02",
"0.02",
"0.01",
"-0.01",
"-0.01",
"-0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative"
] | 171 | 438 | {'C2': '3.00%', 'C3': '9.00%', 'C1': '88.00%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the prediction of C1 by the model for the given test example?"
] | [
"F7",
"F5",
"F2",
"F10",
"F6",
"F1",
"F9",
"F12",
"F8",
"F11",
"F4",
"F3"
] | {'F7': 'Type_of_Cab', 'F5': 'Destination_Type', 'F2': 'Cancellation_Last_1Month', 'F10': 'Trip_Distance', 'F6': 'Customer_Rating', 'F1': 'Life_Style_Index', 'F9': 'Var3', 'F12': 'Var1', 'F8': 'Customer_Since_Months', 'F11': 'Var2', 'F4': 'Gender', 'F3': 'Confidence_Life_Style_Index'} | {'F2': 'F7', 'F6': 'F5', 'F8': 'F2', 'F1': 'F10', 'F7': 'F6', 'F4': 'F1', 'F11': 'F9', 'F9': 'F12', 'F3': 'F8', 'F10': 'F11', 'F12': 'F4', 'F5': 'F3'} | {'C1': 'C2', 'C2': 'C3', 'C3': 'C1'} | C3 | {'C2': 'Low', 'C3': 'Medium', 'C1': 'High'} |
RandomForestClassifier | C2 | Wine Quality Prediction | Based on the input variables, the model is moderately confident that the C2 is the appropriate label for the data under consideration. As a matter of fact, the prediction likelihood associated with class C1 is about 30.42%. The preceeding classification verdict can be largely blamed on the contributions of variables F4, F11, F1, and F6, whereas those with marginally lower contributions are F2, F3, and F7. The variables with moderate contributions are F5, F8, F9, and F10. Considering their respective contributions, F4, F1, F6, and F10 are the variables with positive influence that increase the chances of C2 being the correct label for the given data. The little doubt in the label choice here could be attributed to the negative variables, mainly F11, F5, F9, and F8, which decrease the chances of the model labelling the data given as C2 since these negative variables favour selecting the alternative label, C1 over C2. Given that majority of top variables contribute positively, it is not unexpected that C2 is the picked label with reasonably high confidence. | [
"0.23",
"-0.12",
"0.06",
"0.04",
"-0.03",
"-0.03",
"-0.03",
"0.02",
"-0.01",
"-0.01",
"-0.00"
] | [
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative"
] | 404 | 194 | {'C1': '30.42%', 'C2': '69.58%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F1, F6, F5 and F8) with moderate impact on the prediction made for this test case."
] | [
"F4",
"F11",
"F1",
"F6",
"F5",
"F8",
"F9",
"F10",
"F2",
"F3",
"F7"
] | {'F4': 'alcohol', 'F11': 'sulphates', 'F1': 'volatile acidity', 'F6': 'total sulfur dioxide', 'F5': 'fixed acidity', 'F8': 'citric acid', 'F9': 'residual sugar', 'F10': 'density', 'F2': 'chlorides', 'F3': 'pH', 'F7': 'free sulfur dioxide'} | {'F11': 'F4', 'F10': 'F11', 'F2': 'F1', 'F7': 'F6', 'F1': 'F5', 'F3': 'F8', 'F4': 'F9', 'F8': 'F10', 'F5': 'F2', 'F9': 'F3', 'F6': 'F7'} | {'C1': 'C1', 'C2': 'C2'} | high quality | {'C1': 'low_quality', 'C2': 'high quality'} |
SVC | C1 | E-Commerce Shipping | The classifier is 69.02% certain that the given case is under the class label C1, implying that the likelihood of C2 is only 30.98%. Analysis performed to understand the contribution of each input feature revealed that: F7, F9, and F3 are the most influential features when assigning a label to the given case. Features F4, F6, F8, and F10 have moderate contributions, whereas the F1, F2 and F5 have lower relevance to the final classification decision. F7 and F3 push the class assignment towards C1, whereas F9 does the opposite, decreasing the likelihood of C1. Similar to F9, F4, and F6 negatively impact the C1 classification, whereas F10, F1, and F8 positively push the decision towards the C1 class. Features F2, and F5 all have little impact on the final decision, with F5 having the least impact. | [
"0.11",
"-0.10",
"0.10",
"-0.03",
"-0.01",
"0.01",
"0.01",
"0.01",
"0.01",
"0.00"
] | [
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive"
] | 53 | 421 | {'C1': '69.02%', 'C2': '30.98%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F7 and F9) on the prediction made for this test case.",
"Compare the direction of impact of the features: F3 (value equal to V3), F4 (when it is equal to V1), F6 and F8 (when it is equal to V2).",
"Describe the degree of impact of the following features: F10 (with a value equal to V1), F1 (with a value equal to V0), F2 (when it is equal to V1) and F5 (with a value equal to V4)?"
] | [
"F7",
"F9",
"F3",
"F4",
"F6",
"F8",
"F10",
"F1",
"F2",
"F5"
] | {'F7': 'Weight_in_gms', 'F9': 'Discount_offered', 'F3': 'Prior_purchases', 'F4': 'Customer_care_calls', 'F6': 'Cost_of_the_Product', 'F8': 'Mode_of_Shipment', 'F10': 'Customer_rating', 'F1': 'Gender', 'F2': 'Product_importance', 'F5': 'Warehouse_block'} | {'F3': 'F7', 'F2': 'F9', 'F8': 'F3', 'F6': 'F4', 'F1': 'F6', 'F5': 'F8', 'F7': 'F10', 'F10': 'F1', 'F9': 'F2', 'F4': 'F5'} | {'C1': 'C1', 'C2': 'C2'} | On-time | {'C1': 'On-time', 'C2': 'Late'} |
RandomForestClassifier | C1 | Advertisement Prediction | The classifier trained on this prediction problem assigns a label to a given case based on the information supplied. The class assigned by the classifier to the case under consideration is C1. The probability that C2 is the correct label is around 25.28%; therefore, it is less likely to be the true label. The above classification decision is mainly based on the influence of the features F7, F1, F5, F3, F4, F6, and F2. Of the above stated features, F3 and F1 are the ones shown to have a negative impact, decreasing the odds of C1 being the accurate label for the given case and encouraging the classifier to select C2 instead. Finally, it can be concluded that there is a moderately high level of confidence in the assigned label, which can be attributed to the strong positive contribution of F7 combined with other positive features such as F5 and F4. | [
"0.23",
"-0.18",
"0.03",
"-0.03",
"0.02",
"0.02",
"0.01"
] | [
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive"
] | 31 | 385 | {'C1': '74.72%', 'C2': '25.28%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F7 and F1.",
"Compare and contrast the impact of the following features (F5, F3 (when it is equal to V1), F4 and F6 (when it is equal to V1)) on the model’s prediction of C1.",
"Describe the degree of impact of the following features: F2 (with a value equal to V4)?"
] | [
"F7",
"F1",
"F5",
"F3",
"F4",
"F6",
"F2"
] | {'F7': 'Daily Time Spent on Site', 'F1': 'Daily Internet Usage', 'F5': 'Age', 'F3': 'ad_day', 'F4': 'Area Income', 'F6': 'Gender', 'F2': 'ad_month'} | {'F1': 'F7', 'F4': 'F1', 'F2': 'F5', 'F7': 'F3', 'F3': 'F4', 'F5': 'F6', 'F6': 'F2'} | {'C1': 'C1', 'C2': 'C2'} | Skip | {'C1': 'Skip', 'C2': 'Watch'} |
GradientBoostingClassifier | C1 | Food Ordering Customer Churn Prediction | The case given is labelled as C1 by the classifier with a confidence level equal to 82.07%. Therefore, the probability of C2 being the correct label is only 17.93%. The classification above is mainly due to the contributions of features such as F30, F12, F11, and F32. F17, F24, and F28 are the next three with moderate influence. However, not all the features are considered by the classifier when determining the correct label for the given case. F46, F2, F40, and F8 are notable irrelevant features. With regards to the direction of influence of the relevant features, F30, F12, F11, and F32 are the top features with strong positive contributions favouring the assignment of label C1. The top negative features that shift the classification in a different direction are F17, F24, F33, and F29. Considering the fact that a number of the relevant features have positive attributions, it is not surprising that the classifier is quite certain that the appropriate label is C1 instead of C2. | [
"0.36",
"0.33",
"0.07",
"0.05",
"-0.05",
"-0.04",
"0.03",
"-0.03",
"0.03",
"-0.03",
"0.03",
"-0.03",
"-0.02",
"-0.02",
"0.02",
"0.02",
"-0.02",
"0.02",
"-0.02",
"0.02",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 258 | 168 | {'C2': '17.93%', 'C1': '82.07%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F11, F32, F17 and F24) with moderate impact on the prediction made for this test case."
] | [
"F30",
"F12",
"F11",
"F32",
"F17",
"F24",
"F28",
"F33",
"F9",
"F29",
"F43",
"F3",
"F19",
"F34",
"F39",
"F10",
"F20",
"F1",
"F13",
"F7",
"F46",
"F2",
"F40",
"F8",
"F41",
"F37",
"F18",
"F31",
"F44",
"F35",
"F4",
"F6",
"F38",
"F15",
"F16",
"F45",
"F26",
"F42",
"F21",
"F25",
"F22",
"F5",
"F27",
"F36",
"F14",
"F23"
] | {'F30': 'More restaurant choices', 'F12': 'Ease and convenient', 'F11': 'Bad past experience', 'F32': 'Time saving', 'F17': 'Easy Payment option', 'F24': 'Good Tracking system', 'F28': 'Wrong order delivered', 'F33': 'Influence of rating', 'F9': 'Late Delivery', 'F29': 'Less Delivery time', 'F43': 'Long delivery time', 'F3': 'Delivery person ability', 'F19': 'Order placed by mistake', 'F34': 'More Offers and Discount', 'F39': 'Freshness ', 'F10': 'Unavailability', 'F20': 'Delay of delivery person picking up food', 'F1': 'Poor Hygiene', 'F13': 'Order Time', 'F7': 'Delay of delivery person getting assigned', 'F46': 'High Quality of package', 'F2': 'Residence in busy location', 'F40': 'Good Taste ', 'F8': 'Temperature', 'F41': 'Google Maps Accuracy', 'F37': 'Good Road Condition', 'F18': 'Number of calls', 'F31': 'Low quantity low time', 'F44': 'Politeness', 'F35': 'Maximum wait time', 'F4': 'Age', 'F6': 'Influence of time', 'F38': 'Missing item', 'F15': 'Family size', 'F16': 'Unaffordable', 'F45': 'Health Concern', 'F26': 'Self Cooking', 'F42': 'Good Food quality', 'F21': 'Perference(P2)', 'F25': 'Perference(P1)', 'F22': 'Educational Qualifications', 'F5': 'Monthly Income', 'F27': 'Occupation', 'F36': 'Marital Status', 'F14': 'Gender', 'F23': 'Good Quantity'} | {'F12': 'F30', 'F10': 'F12', 'F21': 'F11', 'F11': 'F32', 'F13': 'F17', 'F16': 'F24', 'F27': 'F28', 'F38': 'F33', 'F19': 'F9', 'F39': 'F29', 'F24': 'F43', 'F37': 'F3', 'F29': 'F19', 'F14': 'F34', 'F43': 'F39', 'F22': 'F10', 'F26': 'F20', 'F20': 'F1', 'F31': 'F13', 'F25': 'F7', 'F40': 'F46', 'F33': 'F2', 'F45': 'F40', 'F44': 'F8', 'F34': 'F41', 'F35': 'F37', 'F41': 'F18', 'F36': 'F31', 'F42': 'F44', 'F32': 'F35', 'F1': 'F4', 'F30': 'F6', 'F28': 'F38', 'F7': 'F15', 'F23': 'F16', 'F18': 'F45', 'F17': 'F26', 'F15': 'F42', 'F9': 'F21', 'F8': 'F25', 'F6': 'F22', 'F5': 'F5', 'F4': 'F27', 'F3': 'F36', 'F2': 'F14', 'F46': 'F23'} | {'C1': 'C2', 'C2': 'C1'} | Go Away | {'C2': 'Return', 'C1': 'Go Away'} |
SVM_linear | C2 | Mobile Price-Range Classification | According to the algorithm, there is little to no chance that the correct label for the given data instance is any of the following classes: C4, C1, and C3. It is very confident that the proper label is C2. This label assignment is largely due to the parts played by the features F19, F12, and F17. On the lower end are the input features F1, F9, F16, and F10, which are shown to be less relevant when it comes to this labelling assignment task. Finally, among the top features identified during the attribution investogation, only F15 and F4 are features with a negative influence, decreasing the odds of C2 being the appropriate label here. | [
"0.78",
"0.14",
"0.11",
"-0.04",
"-0.03",
"0.03",
"0.03",
"0.02",
"-0.02",
"-0.02",
"0.02",
"-0.02",
"-0.01",
"-0.01",
"0.01",
"0.01",
"-0.01",
"-0.01",
"-0.00",
"-0.00"
] | [
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative"
] | 227 | 134 | {'C4': '0.00%', 'C1': '0.00%', 'C3': '0.00%', 'C2': '100.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F18, F5 and F11?"
] | [
"F19",
"F12",
"F17",
"F4",
"F15",
"F14",
"F8",
"F18",
"F5",
"F11",
"F2",
"F3",
"F6",
"F7",
"F13",
"F20",
"F1",
"F9",
"F16",
"F10"
] | {'F19': 'ram', 'F12': 'battery_power', 'F17': 'px_width', 'F4': 'int_memory', 'F15': 'sc_h', 'F14': 'pc', 'F8': 'mobile_wt', 'F18': 'fc', 'F5': 'n_cores', 'F11': 'clock_speed', 'F2': 'blue', 'F3': 'three_g', 'F6': 'touch_screen', 'F7': 'm_dep', 'F13': 'px_height', 'F20': 'talk_time', 'F1': 'dual_sim', 'F9': 'wifi', 'F16': 'four_g', 'F10': 'sc_w'} | {'F11': 'F19', 'F1': 'F12', 'F10': 'F17', 'F4': 'F4', 'F12': 'F15', 'F8': 'F14', 'F6': 'F8', 'F3': 'F18', 'F7': 'F5', 'F2': 'F11', 'F15': 'F2', 'F18': 'F3', 'F19': 'F6', 'F5': 'F7', 'F9': 'F13', 'F14': 'F20', 'F16': 'F1', 'F20': 'F9', 'F17': 'F16', 'F13': 'F10'} | {'C1': 'C4', 'C2': 'C1', 'C3': 'C3', 'C4': 'C2'} | r4 | {'C4': 'r1', 'C1': 'r2', 'C3': 'r3', 'C2': 'r4'} |
SVC | C2 | Paris House Classification | The model predicts that the label for this case is C2 with a high degree of certainty of about 99.19% and the probability of the other label is only 0.81%. From the analysis, the variables with the strongest attributions to this classification decision are F8, F11, and F14. The attributions of these variables increased the response of the model in favour of labelling the case as C2. Other variables that positively supported the label decision include F15, F6, and F16. Not all the variables support the model's prediction of C2 and this is because the values of F2, F10, F3, F7, and F1 are driving the prediction towards C1. The joint attribution from these variables is weaker than that from F8, F11, and F14, so the model is biased toward predicting C2. Finally, F13, F9, F5, and F17 are the least important positive features, given that they have minimal attributions in favour of C2. | [
"0.34",
"0.33",
"0.13",
"-0.03",
"-0.02",
"0.02",
"0.01",
"0.01",
"-0.01",
"0.01",
"-0.01",
"0.01",
"-0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 168 | 94 | {'C2': '99.19%', 'C1': '0.81%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F11, F2, F10 and F15) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F8",
"F14",
"F11",
"F2",
"F10",
"F15",
"F6",
"F16",
"F3",
"F12",
"F7",
"F4",
"F1",
"F13",
"F9",
"F5",
"F17"
] | {'F8': 'isNewBuilt', 'F14': 'hasYard', 'F11': 'hasPool', 'F2': 'hasStormProtector', 'F10': 'hasStorageRoom', 'F15': 'made', 'F6': 'basement', 'F16': 'numberOfRooms', 'F3': 'squareMeters', 'F12': 'floors', 'F7': 'numPrevOwners', 'F4': 'garage', 'F1': 'attic', 'F13': 'cityCode', 'F9': 'price', 'F5': 'cityPartRange', 'F17': 'hasGuestRoom'} | {'F3': 'F8', 'F1': 'F14', 'F2': 'F11', 'F4': 'F2', 'F5': 'F10', 'F12': 'F15', 'F13': 'F6', 'F7': 'F16', 'F6': 'F3', 'F8': 'F12', 'F11': 'F7', 'F15': 'F4', 'F14': 'F1', 'F9': 'F13', 'F17': 'F9', 'F10': 'F5', 'F16': 'F17'} | {'C1': 'C2', 'C2': 'C1'} | Basic | {'C2': 'Basic', 'C1': 'Luxury'} |
LogisticRegression | C2 | Used Cars Price-Range Prediction | According to the output prediction probabilities across the two classes, the output decision for the given data is C2 with a very high confidence level. C1 has a prediction probability of about 0.00%. The variables contributing most to the abovementioned classification are F10, F2, and F5, whereas F4 and F7 are the least influential variables. The very high confidence level associated with the classification decision here could be attributed to the fact that a greater number of the input variables have attributions that increase the model's response towards label C2. F3, F9, and F4 are the variables with negative contributions that attempt to push the model to label this case as C1. To put it in a nutshell, the joint contribution of the negative variables is very low unlike that of the positive variables, hence the model's certainty in the decision here. | [
"0.53",
"0.32",
"0.18",
"0.15",
"0.13",
"0.05",
"-0.04",
"-0.03",
"-0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive"
] | 362 | 192 | {'C2': '100.00%', 'C1': '0.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F4 and F7?"
] | [
"F10",
"F2",
"F5",
"F8",
"F6",
"F1",
"F3",
"F9",
"F4",
"F7"
] | {'F10': 'car_age', 'F2': 'Power', 'F5': 'Fuel_Type', 'F8': 'Engine', 'F6': 'Seats', 'F1': 'Transmission', 'F3': 'Kilometers_Driven', 'F9': 'Name', 'F4': 'Mileage', 'F7': 'Owner_Type'} | {'F5': 'F10', 'F4': 'F2', 'F7': 'F5', 'F3': 'F8', 'F10': 'F6', 'F8': 'F1', 'F1': 'F3', 'F6': 'F9', 'F2': 'F4', 'F9': 'F7'} | {'C1': 'C2', 'C2': 'C1'} | Low | {'C2': 'Low', 'C1': 'High'} |
SVC | C2 | Tic-Tac-Toe Strategy | With a labelling confidence level of 99.50%, the classifier predicts the label C2 in this situation. Hence, it is correct to conclude that the classifier is less certain that C1 is the proper label for the case here. The analysis indicates that five features contradict the decision above, while four features support the classifier. The features contradicting the prediction are usually referred to as negative features while those supporting it are referred to as positive features. The negative features decreasing the odds of C2 being the correct label are F6, F9, F4, F2, and F8. Conversely, the positive features increasing the odds of C2 are F7, F3, F1, and F5. | [
"-0.38",
"0.26",
"0.26",
"0.22",
"-0.22",
"-0.16",
"-0.16",
"0.16",
"-0.01"
] | [
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative"
] | 202 | 117 | {'C1': '0.50%', 'C2': '99.50%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F2, F5 and F8?"
] | [
"F6",
"F7",
"F3",
"F1",
"F9",
"F4",
"F2",
"F5",
"F8"
] | {'F6': 'middle-middle-square', 'F7': 'top-left-square', 'F3': 'bottom-left-square', 'F1': 'bottom-right-square', 'F9': ' top-right-square', 'F4': 'middle-right-square', 'F2': 'top-middle-square', 'F5': 'middle-left-square', 'F8': 'bottom-middle-square'} | {'F5': 'F6', 'F1': 'F7', 'F7': 'F3', 'F9': 'F1', 'F3': 'F9', 'F6': 'F4', 'F2': 'F2', 'F4': 'F5', 'F8': 'F8'} | {'C1': 'C1', 'C2': 'C2'} | player B win | {'C1': 'player B lose', 'C2': 'player B win'} |
SVMClassifier_poly | C2 | Employee Attrition | The class assigned by the model is C2 with a close to 97.67% confidence level, implying that the likelihood of C1 is only 2.33%. Based on the analysis, the most important features considered during the classification are F25, F4, F1, and F28 but among these features, F4 and F1 are the only ones with negative attributions, decreasing the likelihood of C2 being the label for the given case. Furthermore, moderately influencing the decision are F12, F27, F23, and F18. F12, F27, and F23 have positive attributions, while F18 has a negative impact, shifting the prediction in a different direction. Finally, the features with insignificant impact on the model when it comes to this case include F17, F10, F9, and F11. | [
"0.13",
"-0.07",
"-0.04",
"0.04",
"0.04",
"0.03",
"0.03",
"-0.03",
"0.03",
"0.02",
"0.02",
"0.02",
"0.02",
"0.02",
"0.01",
"-0.01",
"0.01",
"0.01",
"0.01",
"-0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 179 | 103 | {'C2': '97.67%', 'C1': '2.33%'} | [
"Summarize the prediction for the given test example?",
"For this test case, summarize the top features influencing the model's decision.",
"For these top features, what are the respective directions of influence on the prediction?",
"Provide a statement on the set of features has limited impact on the prediction of C2 by the model for the given test example?"
] | [
"F25",
"F4",
"F1",
"F28",
"F12",
"F27",
"F23",
"F18",
"F8",
"F19",
"F14",
"F5",
"F20",
"F15",
"F7",
"F26",
"F21",
"F3",
"F13",
"F22",
"F17",
"F10",
"F9",
"F11",
"F24",
"F16",
"F29",
"F2",
"F30",
"F6"
] | {'F25': 'OverTime', 'F4': 'JobSatisfaction', 'F1': 'BusinessTravel', 'F28': 'MaritalStatus', 'F12': 'EnvironmentSatisfaction', 'F27': 'Department', 'F23': 'Age', 'F18': 'YearsInCurrentRole', 'F8': 'TotalWorkingYears', 'F19': 'WorkLifeBalance', 'F14': 'JobLevel', 'F5': 'JobInvolvement', 'F20': 'EducationField', 'F15': 'JobRole', 'F7': 'MonthlyIncome', 'F26': 'PerformanceRating', 'F21': 'DistanceFromHome', 'F3': 'Education', 'F13': 'Gender', 'F22': 'YearsWithCurrManager', 'F17': 'PercentSalaryHike', 'F10': 'RelationshipSatisfaction', 'F9': 'MonthlyRate', 'F11': 'DailyRate', 'F24': 'YearsSinceLastPromotion', 'F16': 'HourlyRate', 'F29': 'YearsAtCompany', 'F2': 'TrainingTimesLastYear', 'F30': 'StockOptionLevel', 'F6': 'NumCompaniesWorked'} | {'F26': 'F25', 'F30': 'F4', 'F17': 'F1', 'F25': 'F28', 'F28': 'F12', 'F21': 'F27', 'F1': 'F23', 'F14': 'F18', 'F11': 'F8', 'F20': 'F19', 'F5': 'F14', 'F29': 'F5', 'F22': 'F20', 'F24': 'F15', 'F6': 'F7', 'F19': 'F26', 'F3': 'F21', 'F27': 'F3', 'F23': 'F13', 'F16': 'F22', 'F9': 'F17', 'F18': 'F10', 'F7': 'F9', 'F2': 'F11', 'F15': 'F24', 'F4': 'F16', 'F13': 'F29', 'F12': 'F2', 'F10': 'F30', 'F8': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | Stay | {'C2': 'Leave', 'C1': 'Leave'} |
KNeighborsClassifier | C1 | Advertisement Prediction | With a higher degree of confidence, the model labels this given case as C1 since there is a zero chance that it is C2. The classification here can be attributed to all the features having positive contributions, decreasing the odds of C2 being the correct label. The features can be ranked based on their degree of influence from the most relevant to the least relevant as follows: F5, F7, F3, F2, F6, F4, F1. This implies that F5 is the most influential feature, while F1 is the least influential among the input features. | [
"0.47",
"0.22",
"0.20",
"0.19",
"0.05",
"0.01",
"0.01"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive"
] | 253 | 163 | {'C2': '0.00%', 'C1': '100.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F1?"
] | [
"F5",
"F7",
"F3",
"F2",
"F6",
"F4",
"F1"
] | {'F5': 'Daily Time Spent on Site', 'F7': 'Area Income', 'F3': 'Age', 'F2': 'Daily Internet Usage', 'F6': 'ad_day', 'F4': 'Gender', 'F1': 'ad_month'} | {'F1': 'F5', 'F3': 'F7', 'F2': 'F3', 'F4': 'F2', 'F7': 'F6', 'F5': 'F4', 'F6': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Watch | {'C2': 'Skip', 'C1': 'Watch'} |
SVM_poly | C1 | Mobile Price-Range Classification | According to the model, C1 has a prediction probability of 99.45 percent, C2 has a prediction probability of 0.47 percent, C4 has a prediction probability of 0.04 percent, and C3 has a prediction probability of 0.05 percent, therefore, the most likely class is C1. F8 and F5 positively influence the above-mentioned label decision in favour of C1, but F7 has the opposite effect, favouring a different label. F9 and F18 both have a similar negative impact on the C1 prediction, whereas F16 has a positive impact. In this case, F4, F1, F13, and F20 have little influence on the labelling result. All in all, the model is confident in its assignment of the C1 class as shown by the predicted probabilities across the classes. | [
"0.78",
"0.11",
"-0.10",
"-0.07",
"0.04",
"-0.04",
"0.03",
"-0.03",
"0.03",
"-0.02",
"-0.02",
"-0.02",
"0.01",
"-0.01",
"-0.01",
"-0.01",
"0.01",
"-0.00",
"-0.00",
"-0.00"
] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative"
] | 47 | 263 | {'C1': '99.45%', 'C2': '0.47%', 'C4': '0.04%', 'C3': '0.05%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F5, F8 and F7.",
"Compare and contrast the impact of the following features (F9, F16 (value equal to V1) and F18 (value equal to V1)) on the model’s prediction of C1.",
"Describe the degree of impact of the following features: F4 (when it is equal to V0), F1, F13 and F20?"
] | [
"F5",
"F8",
"F7",
"F9",
"F16",
"F18",
"F4",
"F1",
"F13",
"F20",
"F17",
"F2",
"F14",
"F3",
"F19",
"F12",
"F10",
"F6",
"F15",
"F11"
] | {'F5': 'ram', 'F8': 'battery_power', 'F7': 'px_height', 'F9': 'px_width', 'F16': 'dual_sim', 'F18': 'four_g', 'F4': 'touch_screen', 'F1': 'int_memory', 'F13': 'pc', 'F20': 'n_cores', 'F17': 'fc', 'F2': 'clock_speed', 'F14': 'three_g', 'F3': 'sc_w', 'F19': 'wifi', 'F12': 'm_dep', 'F10': 'mobile_wt', 'F6': 'talk_time', 'F15': 'sc_h', 'F11': 'blue'} | {'F11': 'F5', 'F1': 'F8', 'F9': 'F7', 'F10': 'F9', 'F16': 'F16', 'F17': 'F18', 'F19': 'F4', 'F4': 'F1', 'F8': 'F13', 'F7': 'F20', 'F3': 'F17', 'F2': 'F2', 'F18': 'F14', 'F13': 'F3', 'F20': 'F19', 'F5': 'F12', 'F6': 'F10', 'F14': 'F6', 'F12': 'F15', 'F15': 'F11'} | {'C1': 'C1', 'C2': 'C2', 'C3': 'C4', 'C4': 'C3'} | r1 | {'C1': 'r1', 'C2': 'r2', 'C4': 'r3', 'C3': 'r4'} |
GradientBoostingClassifier | C1 | Food Ordering Customer Churn Prediction | Per the model employed here, the prediction probability of C2 is only 17.93%, and that of C1 is equal to 82.07%. Given the information provided to the model, the most valid conclusion regarding the true label is that C1 is without a doubt the most likely one. The attributions analysis indicates that F11, F46, F8, F36, and F31 are the major drivers resulting in the prediction probabilities across the classes under consideration. At the tail end are features such as F17, F9, F13, and F19 that have very little influence on the decision made with respect to the given case. Among the influential features, only F11, F46, F36, F7, F29, F35, F6, F26, and F44 have positive contributions in support of labelling the given case as C1. On the other hand, the negative features such as F8, F31, F15, F10, F38, F39, and F16, suggest C2 could likely be the true label in this case. Overall, the marginal doubt in the correctness of assigning C1 to the case under consideration is attributed to the negative features driving the model's decision in the direction of C2 away from C1. But the higher influence of positive features such as F11 and F46 ensures that C1 is assigned as the most probable label. | [
"0.10",
"0.08",
"-0.07",
"0.04",
"-0.04",
"-0.03",
"0.03",
"0.03",
"-0.03",
"0.03",
"-0.03",
"-0.02",
"-0.02",
"0.02",
"-0.02",
"-0.02",
"-0.02",
"0.02",
"0.02",
"-0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 437 | 463 | {'C1': '82.07%', 'C2': '17.93%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F31, F15 and F7) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F11",
"F46",
"F8",
"F36",
"F31",
"F15",
"F7",
"F29",
"F10",
"F35",
"F16",
"F38",
"F39",
"F6",
"F24",
"F30",
"F1",
"F26",
"F44",
"F14",
"F17",
"F9",
"F13",
"F19",
"F45",
"F18",
"F12",
"F42",
"F5",
"F34",
"F2",
"F37",
"F28",
"F23",
"F20",
"F4",
"F25",
"F32",
"F40",
"F22",
"F21",
"F27",
"F43",
"F33",
"F3",
"F41"
] | {'F11': 'Ease and convenient', 'F46': 'More restaurant choices', 'F8': 'Bad past experience', 'F36': 'More Offers and Discount', 'F31': 'Unavailability', 'F15': 'Good Food quality', 'F7': 'Low quantity low time', 'F29': 'Delay of delivery person getting assigned', 'F10': 'Late Delivery', 'F35': 'Less Delivery time', 'F16': 'Residence in busy location', 'F38': 'Freshness ', 'F39': 'Educational Qualifications', 'F6': 'Influence of rating', 'F24': 'Occupation', 'F30': 'Perference(P1)', 'F1': 'Delivery person ability', 'F26': 'Good Taste ', 'F44': 'Long delivery time', 'F14': 'Self Cooking', 'F17': 'Influence of time', 'F9': 'High Quality of package', 'F13': 'Number of calls', 'F19': 'Good Road Condition', 'F45': 'Politeness', 'F18': 'Google Maps Accuracy', 'F12': 'Temperature', 'F42': 'Maximum wait time', 'F5': 'Order Time', 'F34': 'Age', 'F2': 'Order placed by mistake', 'F37': 'Missing item', 'F28': 'Wrong order delivered', 'F23': 'Delay of delivery person picking up food', 'F20': 'Family size', 'F4': 'Unaffordable', 'F25': 'Poor Hygiene', 'F32': 'Health Concern', 'F40': 'Good Tracking system', 'F22': 'Easy Payment option', 'F21': 'Time saving', 'F27': 'Perference(P2)', 'F43': 'Monthly Income', 'F33': 'Marital Status', 'F3': 'Gender', 'F41': 'Good Quantity'} | {'F10': 'F11', 'F12': 'F46', 'F21': 'F8', 'F14': 'F36', 'F22': 'F31', 'F15': 'F15', 'F36': 'F7', 'F25': 'F29', 'F19': 'F10', 'F39': 'F35', 'F33': 'F16', 'F43': 'F38', 'F6': 'F39', 'F38': 'F6', 'F4': 'F24', 'F8': 'F30', 'F37': 'F1', 'F45': 'F26', 'F24': 'F44', 'F17': 'F14', 'F30': 'F17', 'F40': 'F9', 'F41': 'F13', 'F35': 'F19', 'F42': 'F45', 'F34': 'F18', 'F44': 'F12', 'F32': 'F42', 'F31': 'F5', 'F1': 'F34', 'F29': 'F2', 'F28': 'F37', 'F27': 'F28', 'F26': 'F23', 'F7': 'F20', 'F23': 'F4', 'F20': 'F25', 'F18': 'F32', 'F16': 'F40', 'F13': 'F22', 'F11': 'F21', 'F9': 'F27', 'F5': 'F43', 'F3': 'F33', 'F2': 'F3', 'F46': 'F41'} | {'C1': 'C1', 'C2': 'C2'} | Return | {'C1': 'Return', 'C2': 'Go Away'} |
RandomForestClassifier | C1 | Personal Loan Modelling | The model is about 90.0% certain or sure that the correct label based on the input features of the given case is C1. The features with the most significant influence on the decision are F8, F2, F4, and F9. The influence of the features can be categorised as positive or negative traits depending on the direction of the effect on the model. Positive features increase the likelihood of the most likely class (i.e., C1), whereas negative features reduce the model's responsiveness to the assigned label, favouring the less likely class (i.e., C2). From the attribution analysis, F5, F7, and F1 are the negative features here. Overall, the negative features are shown to have moderate to low influence compared to the positive features, hence explaining why the model is very confident about the assigned label C1. | [
"0.47",
"0.23",
"0.20",
"0.08",
"-0.07",
"0.05",
"0.05",
"-0.02",
"-0.01"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 215 | 447 | {'C2': '10.00%', 'C1': '90.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F6, F5 and F1?"
] | [
"F2",
"F8",
"F4",
"F9",
"F7",
"F3",
"F6",
"F5",
"F1"
] | {'F2': 'Income', 'F8': 'CCAvg', 'F4': 'CD Account', 'F9': 'Education', 'F7': 'Extra_service', 'F3': 'Securities Account', 'F6': 'Family', 'F5': 'Mortgage', 'F1': 'Age'} | {'F2': 'F2', 'F4': 'F8', 'F8': 'F4', 'F5': 'F9', 'F9': 'F7', 'F7': 'F3', 'F3': 'F6', 'F6': 'F5', 'F1': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Accept | {'C2': 'Reject', 'C1': 'Accept'} |
LogisticRegression | C1 | Tic-Tac-Toe Strategy | With an 81.01% chance of being correct, C1 is the most likely label, consequently, the C2 class's prediction probability is only 18.99%. The algorithm or classifier got the above prediction mostly due to the influence of features like F7, F5, F4, and F2. F6, which is found to have very little impact with regard to the label choice here, is the least relevant feature for the algorithm. F5, F1, F4, and F2 have a positive direction of influence, pushing the algorithm higher towards the C1 label. Negative features like F7, F9, and F8 favour choosing or labelling the case as C2. | [
"0.28",
"-0.27",
"0.25",
"0.24",
"0.24",
"-0.22",
"-0.21",
"-0.20",
"-0.02"
] | [
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative"
] | 231 | 307 | {'C2': '18.99%', 'C1': '81.01%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F4, F2, F1 and F8) with moderate impact on the prediction made for this test case."
] | [
"F5",
"F7",
"F4",
"F2",
"F1",
"F8",
"F9",
"F3",
"F6"
] | {'F5': 'bottom-right-square', 'F7': 'middle-middle-square', 'F4': 'bottom-left-square', 'F2': 'middle-left-square', 'F1': 'top-left-square', 'F8': ' top-right-square', 'F9': 'middle-right-square', 'F3': 'top-middle-square', 'F6': 'bottom-middle-square'} | {'F9': 'F5', 'F5': 'F7', 'F7': 'F4', 'F4': 'F2', 'F1': 'F1', 'F3': 'F8', 'F6': 'F9', 'F2': 'F3', 'F8': 'F6'} | {'C1': 'C2', 'C2': 'C1'} | player B win | {'C2': 'player B lose', 'C1': 'player B win'} |
SVC | C1 | Student Job Placement | The model makes classification decisions based on the information provided to it and for the case here, the prediction probabilities across the two class labels, C2 and C1, are 49.32% and 50.68%, respectively. Based on these prediction probabilities, the label assigned is C1, since it has the highest likelihood, however, the model is not very certain about the correctness of the assigned label since its probability is marginally higher than the average. The uncertainty in the classification here can be blamed on the fact that only F1, F6, F4, F12, and F5 have positive attributions, shifting the decision higher towards C1. On the other hand, features F3, F2, F10, F7, F9, F11, and F8 have negative contributions that decrease the prediction likelihood of C1 while increasing that of C2. To cut a long story short, the most positive features are F1 and F6, whereas the most negative ones are F3 and F2. Finally, F9, F5, and F11 are not as important as all the previously mentioned features hence received little attention from the model. | [
"0.12",
"-0.12",
"-0.09",
"0.09",
"-0.08",
"0.06",
"-0.06",
"0.05",
"-0.04",
"-0.02",
"0.01",
"-0.00"
] | [
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative"
] | 440 | 204 | {'C2': '49.32%', 'C1': '50.68%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F8, F9 and F5?"
] | [
"F1",
"F3",
"F2",
"F6",
"F10",
"F4",
"F7",
"F12",
"F8",
"F9",
"F5",
"F11"
] | {'F1': 'mba_p', 'F3': 'specialisation', 'F2': 'etest_p', 'F6': 'gender', 'F10': 'workex', 'F4': 'hsc_s', 'F7': 'hsc_p', 'F12': 'degree_t', 'F8': 'ssc_p', 'F9': 'degree_p', 'F5': 'ssc_b', 'F11': 'hsc_b'} | {'F5': 'F1', 'F12': 'F3', 'F4': 'F2', 'F6': 'F6', 'F11': 'F10', 'F9': 'F4', 'F2': 'F7', 'F10': 'F12', 'F1': 'F8', 'F3': 'F9', 'F7': 'F5', 'F8': 'F11'} | {'C1': 'C2', 'C2': 'C1'} | Placed | {'C2': 'Not Placed', 'C1': 'Placed'} |
SVMClassifier_liner | C1 | Employee Attrition | The most likely label for the given case is C1 since the predicted probability of C2 is only 34.27% and this means that the likelihood of C1 is 65.73%. The most relevant features that led to the C1 classification verdict are F5, F30, F26, F17, and F15. However, some of the features are deemed irrelevant to the above verdict and these include F3, F4, F13, and F27. Among the relevant features with some degree of impact, seven are shown to drive the model's class assignment towards the C2, while the remaining support the C1 prediction. Notable negative features swinging the prediction towards C2 are F5, F30, and F26, while the notable positive features are F17 and F15. The small uncertainty associated with the prediction decision for the given case could be attributed to the fact that all the three most important features are negative features whose values contradict assigning the label C1. | [
"-0.14",
"-0.12",
"-0.10",
"0.05",
"0.04",
"-0.04",
"0.04",
"0.04",
"0.04",
"0.03",
"0.03",
"0.02",
"0.02",
"-0.02",
"0.02",
"-0.01",
"-0.01",
"0.01",
"0.01",
"0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 206 | 121 | {'C1': '65.73%', 'C2': '34.27%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F5 and F30.",
"Summarize the direction of influence of the features (F26, F17, F15 and F28) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F5",
"F30",
"F26",
"F17",
"F15",
"F28",
"F14",
"F1",
"F23",
"F9",
"F2",
"F20",
"F19",
"F29",
"F21",
"F18",
"F22",
"F6",
"F12",
"F10",
"F3",
"F4",
"F13",
"F27",
"F16",
"F7",
"F24",
"F11",
"F25",
"F8"
] | {'F5': 'OverTime', 'F30': 'NumCompaniesWorked', 'F26': 'YearsSinceLastPromotion', 'F17': 'BusinessTravel', 'F15': 'MaritalStatus', 'F28': 'RelationshipSatisfaction', 'F14': 'Department', 'F1': 'Age', 'F23': 'Gender', 'F9': 'JobInvolvement', 'F2': 'JobRole', 'F20': 'PerformanceRating', 'F19': 'EnvironmentSatisfaction', 'F29': 'DailyRate', 'F21': 'YearsAtCompany', 'F18': 'YearsWithCurrManager', 'F22': 'Education', 'F6': 'EducationField', 'F12': 'WorkLifeBalance', 'F10': 'DistanceFromHome', 'F3': 'YearsInCurrentRole', 'F4': 'TrainingTimesLastYear', 'F13': 'TotalWorkingYears', 'F27': 'StockOptionLevel', 'F16': 'PercentSalaryHike', 'F7': 'MonthlyRate', 'F24': 'MonthlyIncome', 'F11': 'JobLevel', 'F25': 'HourlyRate', 'F8': 'JobSatisfaction'} | {'F26': 'F5', 'F8': 'F30', 'F15': 'F26', 'F17': 'F17', 'F25': 'F15', 'F18': 'F28', 'F21': 'F14', 'F1': 'F1', 'F23': 'F23', 'F29': 'F9', 'F24': 'F2', 'F19': 'F20', 'F28': 'F19', 'F2': 'F29', 'F13': 'F21', 'F16': 'F18', 'F27': 'F22', 'F22': 'F6', 'F20': 'F12', 'F3': 'F10', 'F14': 'F3', 'F12': 'F4', 'F11': 'F13', 'F10': 'F27', 'F9': 'F16', 'F7': 'F7', 'F6': 'F24', 'F5': 'F11', 'F4': 'F25', 'F30': 'F8'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Leave', 'C2': 'Leave'} |
RandomForestClassifier | C2 | Printer Sales | Per the classifier for the given data, the most plausible label is C2. F2, F19, F24, and F8 are the main features pushing for the above-mentioned outcome. F3, F18, F14, F16, F13, and F22, on the other hand, have little contribution to the classifier employed here. F25, F17, F7, and F9 have a moderate contribution to the assignment of C2. The classifier's confidence in the label decision above can be attributed to larger positive attributions of F17, F25, F24, and F19 compared to the negative attributions of F7, F21, F2, F4, F8, and F5. | [
"0.22",
"0.13",
"-0.10",
"-0.05",
"-0.04",
"0.03",
"0.02",
"0.02",
"0.02",
"0.02",
"0.02",
"0.01",
"-0.01",
"0.01",
"-0.01",
"0.01",
"0.01",
"-0.01",
"0.01",
"0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 242 | 319 | {'C1': '20.00%', 'C2': '80.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F2, F8, F7 and F17) with moderate impact on the prediction made for this test case."
] | [
"F19",
"F24",
"F2",
"F8",
"F7",
"F17",
"F25",
"F9",
"F11",
"F23",
"F10",
"F20",
"F4",
"F1",
"F21",
"F15",
"F26",
"F5",
"F12",
"F6",
"F16",
"F22",
"F14",
"F3",
"F13",
"F18"
] | {'F19': 'X24', 'F24': 'X1', 'F2': 'X8', 'F8': 'X21', 'F7': 'X4', 'F17': 'X10', 'F25': 'X3', 'F9': 'X15', 'F11': 'X9', 'F23': 'X23', 'F10': 'X25', 'F20': 'X7', 'F4': 'X22', 'F1': 'X11', 'F21': 'X17', 'F15': 'X18', 'F26': 'X26', 'F5': 'X13', 'F12': 'X6', 'F6': 'X20', 'F16': 'X16', 'F22': 'X19', 'F14': 'X2', 'F3': 'X12', 'F13': 'X5', 'F18': 'X14'} | {'F24': 'F19', 'F1': 'F24', 'F8': 'F2', 'F21': 'F8', 'F4': 'F7', 'F10': 'F17', 'F3': 'F25', 'F15': 'F9', 'F9': 'F11', 'F23': 'F23', 'F25': 'F10', 'F7': 'F20', 'F22': 'F4', 'F11': 'F1', 'F17': 'F21', 'F18': 'F15', 'F26': 'F26', 'F13': 'F5', 'F6': 'F12', 'F20': 'F6', 'F16': 'F16', 'F19': 'F22', 'F2': 'F14', 'F12': 'F3', 'F5': 'F13', 'F14': 'F18'} | {'C1': 'C1', 'C2': 'C2'} | More | {'C1': 'Less', 'C2': 'More'} |
SGDClassifier | C1 | Job Change of Data Scientists | The least probable class, according to the classification algorithm, is C2, with a prediction probability of 25.12%, therefore, we can conclude that the algorithm is quite confident that the correct label for this data is C1. Analysing the attributions revealed that F1, F6, F8, and F2 are the most relevant features, whereas F5, F7, and F4 are the least relevant features. Increasing the algorithm's response in favour of C1 are the positive features F1, F8, F2, F7, F5, and F9. On the contrary, all the other features, F6, F3, F12, F11, F10, and F4, drive the algorithm towards labelling the given data as C2, hence they are considered negative features. Furthermore, the negative influence on the algorithm is the reason why the confidence level in the C1 is reduced to 74.88%. | [
"0.14",
"0.10",
"-0.07",
"0.07",
"-0.04",
"-0.03",
"-0.02",
"0.02",
"-0.01",
"0.01",
"0.01",
"-0.00"
] | [
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative"
] | 223 | 449 | {'C1': '74.88%', 'C2': '25.12%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F9, F10 and F5?"
] | [
"F1",
"F8",
"F6",
"F2",
"F3",
"F12",
"F11",
"F9",
"F10",
"F5",
"F7",
"F4"
] | {'F1': 'city_development_index', 'F8': 'relevent_experience', 'F6': 'city', 'F2': 'major_discipline', 'F3': 'experience', 'F12': 'training_hours', 'F11': 'education_level', 'F9': 'gender', 'F10': 'enrolled_university', 'F5': 'company_type', 'F7': 'last_new_job', 'F4': 'company_size'} | {'F1': 'F1', 'F5': 'F8', 'F3': 'F6', 'F8': 'F2', 'F9': 'F3', 'F2': 'F12', 'F7': 'F11', 'F4': 'F9', 'F6': 'F10', 'F11': 'F5', 'F12': 'F7', 'F10': 'F4'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Stay', 'C2': 'Leave'} |
SVM_poly | C4 | Mobile Price-Range Classification | According to the classification algorithm, neither C1 nor C3 nor C2 is the correct label for the given case. It is 100.0% certain that C4 is the right label. The higher degree of certainty in the above prediction can be attributed to the positive contributions of F18, F11, and F12. The other positive features include F20, F15, F6, and F14, however, unlike F18, F11, and F12, these features have a moderately low impact on the algorithm's decision. The remaining positive features, F7, F19, F13, and F9, are among the least influential input features considered by the algorithm. There are other features such as F16, F10, F3, and F4 whose contributions only serve to decrease the odds of C4 being the correct label for the given case. Regarding the high confidence of the algorithm with respect to this classification, one can conclude that the negative features have little influence on the algorithm's label decision here. | [
"0.77",
"0.14",
"0.13",
"-0.04",
"-0.04",
"-0.03",
"0.03",
"-0.02",
"0.02",
"-0.02",
"-0.02",
"-0.02",
"0.02",
"-0.01",
"0.01",
"0.01",
"-0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive"
] | 251 | 161 | {'C1': '0.00%', 'C3': '0.00%', 'C2': '0.00%', 'C4': '100.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F16, F10 and F3) with moderate impact on the prediction made for this test case."
] | [
"F18",
"F11",
"F12",
"F16",
"F10",
"F3",
"F20",
"F4",
"F15",
"F17",
"F5",
"F2",
"F6",
"F8",
"F14",
"F7",
"F1",
"F19",
"F13",
"F9"
] | {'F18': 'ram', 'F11': 'battery_power', 'F12': 'px_width', 'F16': 'int_memory', 'F10': 'sc_h', 'F3': 'wifi', 'F20': 'fc', 'F4': 'three_g', 'F15': 'mobile_wt', 'F17': 'clock_speed', 'F5': 'm_dep', 'F2': 'n_cores', 'F6': 'pc', 'F8': 'touch_screen', 'F14': 'blue', 'F7': 'talk_time', 'F1': 'sc_w', 'F19': 'px_height', 'F13': 'four_g', 'F9': 'dual_sim'} | {'F11': 'F18', 'F1': 'F11', 'F10': 'F12', 'F4': 'F16', 'F12': 'F10', 'F20': 'F3', 'F3': 'F20', 'F18': 'F4', 'F6': 'F15', 'F2': 'F17', 'F5': 'F5', 'F7': 'F2', 'F8': 'F6', 'F19': 'F8', 'F15': 'F14', 'F14': 'F7', 'F13': 'F1', 'F9': 'F19', 'F17': 'F13', 'F16': 'F9'} | {'C1': 'C1', 'C2': 'C3', 'C3': 'C2', 'C4': 'C4'} | r4 | {'C1': 'r1', 'C3': 'r2', 'C2': 'r3', 'C4': 'r4'} |
DNN | C2 | Ethereum Fraud Detection | The prediction likelihoods across the two classes are 15.35% for class C1 and 84.65% for C2, it can be concluded that C2 is the most probable class label for the given data instance. According to the attribution analysis conducted, the different input variables have varying degrees of influence on the model's decision here. The most influential set of variables is F18, F31, F19, F36, F35, F20, and F38, while the variables with the least influence include F37, F3, F25, F16, F33, and F6. The following or subsequent analysis performed to understand the direction of contribution of of the features will focus on the most influential ones controlling the label selection here. Among the top influential features, F18, F31, F19, F36, and F38, only F18 and F31 have negative contributions, decreasing the probability that C2 is the correct label, and they strongly support labelling the case as C1 instead. Pushing the classification decision in favour of C2 are the positive variables such as F19, F36, and F38. The contributions of the remaining variables, including F35, F20, and F5, have moderate to low influence. All in all, the marginal uncertainty in the decision here is mainly due to the negative influences of F18, F31, F9, and F23, but the positive contributions of F19, F36, F5, F35, F20, and F38 drive the decision higher towards C2. | [
"-5.85",
"-5.52",
"2.13",
"2.13",
"2.11",
"1.50",
"1.39",
"1.33",
"-1.31",
"-1.15",
"0.90",
"-0.53",
"-0.46",
"0.46",
"0.42",
"0.40",
"0.35",
"-0.25",
"0.18",
"0.16",
"0.15",
"-0.15",
"0.12",
"-0.12",
"0.12",
"-0.07",
"0.07",
"0.07",
"-0.06",
"-0.06",
"-0.05",
"-0.05",
"0.03",
"0.03",
"0.02",
"-0.01",
"-0.01",
"0.00"
] | [
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive"
] | 413 | 200 | {'C1': '15.35%', 'C2': '84.65%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F18, F31, F19, F36 and F38.",
"Summarize the direction of influence of the features (F35, F20 and F5) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F18",
"F31",
"F19",
"F36",
"F38",
"F35",
"F20",
"F5",
"F9",
"F23",
"F22",
"F17",
"F7",
"F28",
"F8",
"F21",
"F24",
"F10",
"F1",
"F32",
"F30",
"F15",
"F26",
"F2",
"F13",
"F34",
"F27",
"F4",
"F11",
"F14",
"F29",
"F12",
"F37",
"F3",
"F25",
"F16",
"F33",
"F6"
] | {'F18': ' ERC20 uniq rec contract addr', 'F31': ' ERC20 uniq rec token name', 'F19': 'min value received', 'F36': 'Time Diff between first and last (Mins)', 'F38': 'avg val sent', 'F35': ' ERC20 uniq sent token name', 'F20': 'Sent tnx', 'F5': 'Avg min between received tnx', 'F9': 'Unique Received From Addresses', 'F23': ' ERC20 uniq rec addr', 'F22': 'total transactions (including tnx to create contract', 'F17': 'Avg min between sent tnx', 'F7': ' ERC20 uniq sent addr.1', 'F28': 'avg val received', 'F8': 'Unique Sent To Addresses', 'F21': 'max value received ', 'F24': 'max val sent', 'F10': 'min val sent', 'F1': 'Number of Created Contracts', 'F32': 'total ether received', 'F30': ' ERC20 uniq sent addr', 'F15': ' ERC20 total Ether received', 'F26': 'Received Tnx', 'F2': ' ERC20 avg val sent', 'F13': 'total Ether sent', 'F34': ' ERC20 min val sent', 'F27': 'max val sent to contract', 'F4': 'total ether balance', 'F11': ' ERC20 max val sent', 'F14': ' Total ERC20 tnxs', 'F29': ' ERC20 total ether sent', 'F12': ' ERC20 avg val rec', 'F37': 'avg value sent to contract', 'F3': ' ERC20 min val rec', 'F25': ' ERC20 max val rec', 'F16': ' ERC20 total Ether sent contract', 'F33': 'min value sent to contract', 'F6': 'total ether sent contracts'} | {'F30': 'F18', 'F38': 'F31', 'F9': 'F19', 'F3': 'F36', 'F14': 'F38', 'F37': 'F35', 'F4': 'F20', 'F2': 'F5', 'F7': 'F9', 'F28': 'F23', 'F18': 'F22', 'F1': 'F17', 'F29': 'F7', 'F11': 'F28', 'F8': 'F8', 'F10': 'F21', 'F13': 'F24', 'F12': 'F10', 'F6': 'F1', 'F20': 'F32', 'F27': 'F30', 'F24': 'F15', 'F5': 'F26', 'F36': 'F2', 'F19': 'F13', 'F34': 'F34', 'F16': 'F27', 'F22': 'F4', 'F35': 'F11', 'F23': 'F14', 'F25': 'F29', 'F33': 'F12', 'F17': 'F37', 'F31': 'F3', 'F32': 'F25', 'F26': 'F16', 'F15': 'F33', 'F21': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
KNeighborsClassifier | C2 | Credit Risk Classification | According to the machine learning model, it is more likely that the case's label is C2, with a certainty of 100.0%, and this prediction decision is mainly based on the effects of the following features: F8, F10, F6, F9, and F1 on the model. Apart from F1 and F9, all the other variables mentioned above have a strong positive influence, improving the odds of the prediction class, C2. Together with F1 and F9, the values of variables F3 and F2 indicate that C1 could be the correct label instead. Unlike the top positive variables, F8, F10, and F6, each of these negative variables has a moderate contribution to the final decision. The features F5, F7, F11, and F4 are shown to have made minor contributions to the model's decision in this case. In summary, with only the positive contributions from F8, F10, F6, F11, and F5, the model is very certain of the classification output as indicated by the predicted probabilities across C2 and C1. | [
"0.09",
"0.03",
"0.02",
"-0.02",
"-0.02",
"-0.02",
"-0.01",
"0.01",
"-0.01",
"-0.01",
"0.00"
] | [
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive"
] | 115 | 290 | {'C2': '100.00%', 'C1': '0.00%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F8, F10, F6 and F1) on the prediction made for this test case.",
"Compare the direction of impact of the features: F9, F3 and F2.",
"Describe the degree of impact of the following features: F5, F7 and F4?"
] | [
"F8",
"F10",
"F6",
"F1",
"F9",
"F3",
"F2",
"F5",
"F7",
"F4",
"F11"
] | {'F8': 'fea_4', 'F10': 'fea_8', 'F6': 'fea_2', 'F1': 'fea_9', 'F9': 'fea_6', 'F3': 'fea_10', 'F2': 'fea_1', 'F5': 'fea_7', 'F7': 'fea_11', 'F4': 'fea_3', 'F11': 'fea_5'} | {'F4': 'F8', 'F8': 'F10', 'F2': 'F6', 'F9': 'F1', 'F6': 'F9', 'F10': 'F3', 'F1': 'F2', 'F7': 'F5', 'F11': 'F7', 'F3': 'F4', 'F5': 'F11'} | {'C1': 'C2', 'C2': 'C1'} | Low | {'C2': 'Low', 'C1': 'High'} |
SVMClassifier_poly | C1 | Employee Attrition | The classification findings by the model for the case here are as follows: there is a 97.67% chance that C1 is the correct label hence only a marginally low chance of 2.33% that C1 is not the correct label but C2 is. From the above findings, it is valid to conclude that the right class for the given case is C1, and the model is very certain of this decision. The features with the most control and influence on the classification above are F27, F4, F17, F22, and F7 but the influence of the remaining features is either moderate or low or negligible. Some of the features with moderate impact include F14, F11, F15, and F23. Those with low influence are F18, F10, F25, F9, and F8. Finally, those with negligible impact are F19, F3, F1, F6, F5, F20, F29, F28, F2, and F13 since their values are shown to have no impact on the classification made by the model here. The top positive features increasing the prediction likelihood of class C1 are F27, F7, and F24. Conversely, the negative features decreasing the odds in favour of C2 are primarily F4, F15, and F17. | [
"0.13",
"-0.07",
"-0.04",
"0.04",
"0.04",
"0.03",
"0.03",
"-0.03",
"0.03",
"0.02",
"0.02",
"0.02",
"0.02",
"0.02",
"0.01",
"-0.01",
"0.01",
"0.01",
"0.01",
"-0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 254 | 164 | {'C1': '97.67%', 'C2': '2.33%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F11, F15, F23 and F16?"
] | [
"F27",
"F4",
"F17",
"F22",
"F7",
"F14",
"F11",
"F15",
"F23",
"F16",
"F24",
"F26",
"F12",
"F30",
"F21",
"F18",
"F10",
"F25",
"F9",
"F8",
"F19",
"F3",
"F1",
"F6",
"F5",
"F20",
"F29",
"F28",
"F2",
"F13"
] | {'F27': 'OverTime', 'F4': 'JobSatisfaction', 'F17': 'BusinessTravel', 'F22': 'MaritalStatus', 'F7': 'EnvironmentSatisfaction', 'F14': 'Department', 'F11': 'Age', 'F15': 'YearsInCurrentRole', 'F23': 'TotalWorkingYears', 'F16': 'WorkLifeBalance', 'F24': 'JobLevel', 'F26': 'JobInvolvement', 'F12': 'EducationField', 'F30': 'JobRole', 'F21': 'MonthlyIncome', 'F18': 'PerformanceRating', 'F10': 'DistanceFromHome', 'F25': 'Education', 'F9': 'Gender', 'F8': 'YearsWithCurrManager', 'F19': 'PercentSalaryHike', 'F3': 'RelationshipSatisfaction', 'F1': 'MonthlyRate', 'F6': 'DailyRate', 'F5': 'YearsSinceLastPromotion', 'F20': 'HourlyRate', 'F29': 'YearsAtCompany', 'F28': 'TrainingTimesLastYear', 'F2': 'StockOptionLevel', 'F13': 'NumCompaniesWorked'} | {'F26': 'F27', 'F30': 'F4', 'F17': 'F17', 'F25': 'F22', 'F28': 'F7', 'F21': 'F14', 'F1': 'F11', 'F14': 'F15', 'F11': 'F23', 'F20': 'F16', 'F5': 'F24', 'F29': 'F26', 'F22': 'F12', 'F24': 'F30', 'F6': 'F21', 'F19': 'F18', 'F3': 'F10', 'F27': 'F25', 'F23': 'F9', 'F16': 'F8', 'F9': 'F19', 'F18': 'F3', 'F7': 'F1', 'F2': 'F6', 'F15': 'F5', 'F4': 'F20', 'F13': 'F29', 'F12': 'F28', 'F10': 'F2', 'F8': 'F13'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Leave', 'C2': 'Leave'} |
LogisticRegression | C3 | Flight Price-Range Classification | The model is very confident that C3 is the most probable class for the given case, with a probability of 90.48% which means that the other labels are very unlikely. F12 and F1 are the most important variables with respect to this classification verdict while all other variables are shown to have a medium or low impact. Fortunately, the top variables, F12 and F1, have the same direction of influence, increasing the likelihood of C3. Furthermore, while F4 and F11 push the model to predict C3, those pushing for the assignment of a different label are F3, F8, and F2. Finally, many features have a fairly small impact on the final prediction made by the model here, but F7, F8, and F6 have the least impact. | [
"0.40",
"0.35",
"0.11",
"0.05",
"-0.04",
"0.03",
"0.03",
"-0.02",
"0.02",
"0.02",
"-0.01",
"-0.01"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative"
] | 89 | 244 | {'C3': '90.48%', 'C1': '9.51%', 'C2': '0.01%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F12 (equal to V4) and F1 (equal to V3).",
"Summarize the direction of influence of the features (F11 (equal to V2), F4, F3 (when it is equal to V0) and F5) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F12",
"F1",
"F11",
"F4",
"F3",
"F5",
"F10",
"F2",
"F9",
"F7",
"F8",
"F6"
] | {'F12': 'Total_Stops', 'F1': 'Airline', 'F11': 'Destination', 'F4': 'Arrival_hour', 'F3': 'Source', 'F5': 'Duration_hours', 'F10': 'Dep_hour', 'F2': 'Dep_minute', 'F9': 'Arrival_minute', 'F7': 'Journey_month', 'F8': 'Journey_day', 'F6': 'Duration_mins'} | {'F12': 'F12', 'F9': 'F1', 'F11': 'F11', 'F5': 'F4', 'F10': 'F3', 'F7': 'F5', 'F3': 'F10', 'F4': 'F2', 'F6': 'F9', 'F2': 'F7', 'F1': 'F8', 'F8': 'F6'} | {'C1': 'C3', 'C2': 'C1', 'C3': 'C2'} | Low | {'C3': 'Low', 'C1': 'Moderate', 'C2': 'High'} |
SVC | C1 | Water Quality Classification | Despite the reasonably high confidence in the assigned label, the prediction probabilities across the two classes indicate that C2 might be the correct label. F7, F2, F9, and F8 are the factors whose major contributions resulted in the labelling choice mentioned above. According to the analysis, the top two factors, F7 and F2, have a negative influence, leading the classifier to classify the data as C2 rather than C1. F1 is the only other negative variable with a moderate effect when compared to the other two negative variables. Nevertheless, there are several factors, F9, F8, F3, F6, F4, and F5, that favourably support and encourage the classifier to assign C1. All in all, the degree of uncertainty in this classification instance might be explained by just looking at the negative factors' rather strong pull on the classifier towards C2. | [
"-0.01",
"-0.01",
"0.01",
"0.01",
"-0.01",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 237 | 326 | {'C2': '38.68%', 'C1': '61.32%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F6, F4 and F5?"
] | [
"F7",
"F2",
"F9",
"F8",
"F1",
"F3",
"F6",
"F4",
"F5"
] | {'F7': 'Sulfate', 'F2': 'Hardness', 'F9': 'ph', 'F8': 'Conductivity', 'F1': 'Turbidity', 'F3': 'Chloramines', 'F6': 'Solids', 'F4': 'Trihalomethanes', 'F5': 'Organic_carbon'} | {'F5': 'F7', 'F2': 'F2', 'F1': 'F9', 'F6': 'F8', 'F9': 'F1', 'F4': 'F3', 'F3': 'F6', 'F8': 'F4', 'F7': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Portable | {'C2': 'Not Portable', 'C1': 'Portable'} |
MLPClassifier | C2 | Ethereum Fraud Detection | C1 has a probability estimate of only 6.80%, while that of C2 is 93.20%; consequently, the most likely class for the given case is C2. The important or relevant features considered by the classifier are F12, F17, F8, F16, F9, F24, F14, F34, F23, F29, F1, F38, F37, F18, F6, F22, F25, F7, F10, and F4. Not all input features are relevant when determining the appropriate label and these irrelevant features include F32, F13, and F28. Furthermore, F12 and F17 have a strong positive effect, increasing the odds in favour of C2. In contrast, the F8, F9, and F16 are the negative features, lowering the odds of C2. Comparing the attributions of F12, F24, and F17 features to those of the negative features mentioned above, it is not surprising that the classifier is convinced that C2 is the most likely label here. | [
"0.14",
"0.10",
"-0.08",
"-0.07",
"-0.07",
"0.07",
"0.06",
"-0.06",
"-0.06",
"0.06",
"-0.05",
"-0.05",
"-0.05",
"0.03",
"-0.02",
"-0.02",
"0.02",
"0.02",
"-0.01",
"0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 243 | 317 | {'C1': '6.80%', 'C2': '93.20%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F14, F34, F23 and F29?"
] | [
"F12",
"F17",
"F8",
"F16",
"F9",
"F24",
"F14",
"F34",
"F23",
"F29",
"F1",
"F38",
"F37",
"F18",
"F6",
"F22",
"F25",
"F7",
"F10",
"F4",
"F32",
"F28",
"F13",
"F27",
"F26",
"F36",
"F15",
"F11",
"F2",
"F33",
"F35",
"F20",
"F3",
"F30",
"F19",
"F5",
"F21",
"F31"
] | {'F12': 'Unique Received From Addresses', 'F17': ' ERC20 total Ether sent contract', 'F8': 'total ether received', 'F16': 'Sent tnx', 'F9': 'Number of Created Contracts', 'F24': ' ERC20 uniq rec token name', 'F14': ' ERC20 uniq rec contract addr', 'F34': 'max value received ', 'F23': 'total transactions (including tnx to create contract', 'F29': ' ERC20 uniq sent addr.1', 'F1': ' ERC20 uniq sent addr', 'F38': 'Received Tnx', 'F37': 'avg val received', 'F18': ' ERC20 uniq rec addr', 'F6': 'avg val sent', 'F22': 'min value received', 'F25': 'Unique Sent To Addresses', 'F7': ' ERC20 uniq sent token name', 'F10': 'Avg min between received tnx', 'F4': 'Time Diff between first and last (Mins)', 'F32': ' ERC20 min val rec', 'F28': ' ERC20 max val rec', 'F13': ' ERC20 min val sent', 'F27': ' ERC20 max val sent', 'F26': ' ERC20 avg val sent', 'F36': ' ERC20 avg val rec', 'F15': ' Total ERC20 tnxs', 'F11': ' ERC20 total ether sent', 'F2': ' ERC20 total Ether received', 'F33': 'total ether balance', 'F35': 'total ether sent contracts', 'F20': 'total Ether sent', 'F3': 'avg value sent to contract', 'F30': 'max val sent to contract', 'F19': 'min value sent to contract', 'F5': 'max val sent', 'F21': 'min val sent', 'F31': 'Avg min between sent tnx'} | {'F7': 'F12', 'F26': 'F17', 'F20': 'F8', 'F4': 'F16', 'F6': 'F9', 'F38': 'F24', 'F30': 'F14', 'F10': 'F34', 'F18': 'F23', 'F29': 'F29', 'F27': 'F1', 'F5': 'F38', 'F11': 'F37', 'F28': 'F18', 'F14': 'F6', 'F9': 'F22', 'F8': 'F25', 'F37': 'F7', 'F2': 'F10', 'F3': 'F4', 'F31': 'F32', 'F32': 'F28', 'F34': 'F13', 'F35': 'F27', 'F36': 'F26', 'F33': 'F36', 'F23': 'F15', 'F25': 'F11', 'F24': 'F2', 'F22': 'F33', 'F21': 'F35', 'F19': 'F20', 'F17': 'F3', 'F16': 'F30', 'F15': 'F19', 'F13': 'F5', 'F12': 'F21', 'F1': 'F31'} | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
BernoulliNB | C2 | German Credit Evaluation | The model is not 100% convinced that the correct label for the data under consideration is C2 since there is a 26.27% chance that labelling the data as C1 is correct. All the input variables are shown to have some degree of influence on the classification decision, with the most influential variables being F9, F2, and F7, whereas F6 and F1 are the least influential. The impact of F5, F3, F4, and F8 can be considered moderate compared to the F9, F2, and F7. The uncertainty surrounding the above classification can be blamed on the fact that the majority of input variables have values suggesting that C1 could be the appropriate label. The negative features that decrease the prediction likelihood of C2 are F9, F7, F4, and F8. However, given that the prediction probability is about 73.73%, it can be said that the influence of positive features, F2, F5, F3, and F6, is enough to swing the model's verdict in favour of C2. | [
"-0.23",
"0.18",
"-0.15",
"0.10",
"0.06",
"-0.05",
"-0.05",
"0.02",
"-0.02"
] | [
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative"
] | 295 | 185 | {'C2': '73.73%', 'C1': '26.27%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F4, F8 and F6) with moderate impact on the prediction made for this test case."
] | [
"F9",
"F2",
"F7",
"F5",
"F3",
"F4",
"F8",
"F6",
"F1"
] | {'F9': 'Saving accounts', 'F2': 'Sex', 'F7': 'Housing', 'F5': 'Purpose', 'F3': 'Checking account', 'F4': 'Job', 'F8': 'Duration', 'F6': 'Age', 'F1': 'Credit amount'} | {'F5': 'F9', 'F2': 'F2', 'F4': 'F7', 'F9': 'F5', 'F6': 'F3', 'F3': 'F4', 'F8': 'F8', 'F1': 'F6', 'F7': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Good Credit | {'C2': 'Good Credit', 'C1': 'Bad Credit'} |
SVMClassifier_poly | C1 | Employee Attrition | The model predicted class C1 with an 81.98% prediction likelihood. F24 had the largest impact, followed by F23, F9, F18, F14, F10, F11, F2, F8, F21, F20, F27, F4, F12, F15, F19, F13, F16, F30, and finally, F29, which had the smallest non-zero impact. F24, the feature with the largest impact, contributed against the direction of the prediction, whereas F23, F9, F18, and F14 all contributed positively towards the prediction. Other features that had a negative influence on the prediction included F11 and F2, whereas F10 had a positive influence on the prediction. F22, F6, F1, and F5 are shown to have close to zero attribution in the model's prediction verdict in the given case. | [
"-0.13",
"0.06",
"0.05",
"0.04",
"0.04",
"0.04",
"-0.04",
"0.03",
"-0.03",
"0.03",
"-0.03",
"0.02",
"-0.02",
"-0.02",
"-0.02",
"0.02",
"0.01",
"0.01",
"0.01",
"-0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 98 | 44 | {'C1': '81.98%', 'C2': '18.02%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F24 (with a value equal to V1), F23 (equal to V3), F9 (with a value equal to V0), F18 (equal to V1) and F14.",
"Summarize the direction of influence of the features (F10 (value equal to V0), F11 (value equal to V2) and F2 (value equal to V3)) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F24",
"F23",
"F9",
"F18",
"F14",
"F10",
"F11",
"F2",
"F8",
"F21",
"F20",
"F27",
"F4",
"F12",
"F15",
"F19",
"F13",
"F16",
"F30",
"F29",
"F22",
"F6",
"F1",
"F5",
"F26",
"F3",
"F25",
"F7",
"F17",
"F28"
] | {'F24': 'OverTime', 'F23': 'JobSatisfaction', 'F9': 'MaritalStatus', 'F18': 'Department', 'F14': 'NumCompaniesWorked', 'F10': 'BusinessTravel', 'F11': 'JobRole', 'F2': 'EnvironmentSatisfaction', 'F8': 'YearsInCurrentRole', 'F21': 'JobInvolvement', 'F20': 'WorkLifeBalance', 'F27': 'YearsSinceLastPromotion', 'F4': 'TotalWorkingYears', 'F12': 'JobLevel', 'F15': 'Age', 'F19': 'EducationField', 'F13': 'PerformanceRating', 'F16': 'MonthlyRate', 'F30': 'Education', 'F29': 'MonthlyIncome', 'F22': 'DailyRate', 'F6': 'YearsAtCompany', 'F1': 'RelationshipSatisfaction', 'F5': 'TrainingTimesLastYear', 'F26': 'StockOptionLevel', 'F3': 'Gender', 'F25': 'PercentSalaryHike', 'F7': 'HourlyRate', 'F17': 'DistanceFromHome', 'F28': 'YearsWithCurrManager'} | {'F26': 'F24', 'F30': 'F23', 'F25': 'F9', 'F21': 'F18', 'F8': 'F14', 'F17': 'F10', 'F24': 'F11', 'F28': 'F2', 'F14': 'F8', 'F29': 'F21', 'F20': 'F20', 'F15': 'F27', 'F11': 'F4', 'F5': 'F12', 'F1': 'F15', 'F22': 'F19', 'F19': 'F13', 'F7': 'F16', 'F27': 'F30', 'F6': 'F29', 'F2': 'F22', 'F13': 'F6', 'F18': 'F1', 'F12': 'F5', 'F10': 'F26', 'F23': 'F3', 'F9': 'F25', 'F4': 'F7', 'F3': 'F17', 'F16': 'F28'} | {'C1': 'C1', 'C2': 'C2'} | Stay | {'C1': 'Leave', 'C2': 'Leave'} |
SVC | C3 | German Credit Evaluation | This case's label has a 70.83 percent chance of being C3 and per the predicted likelihoods across the alternative labels, C1 has a 29.71 percent chance of being the correct label, however, the model is certain that C2 is not the true label. The most important variables are F1, F7, F3, and F2, whereas the remaining influential variables are listed in order of the magnitude of their contributions: F8, F6, F4, F9, and F5. Three of the nine variables have values that push towards the prediction of label C1 while the other attributes are referred to as positive since their values inspire the prediction of class C3. F1, F7, and F3 are the three attributes that have a negative influence on the prediction judgement, pushing it away from C3 towards the label C1. Finally, it is essential to highlight that the cumulative effect of positive attributes is greater than that of negative attributes, F3, F7, and F1. | [
"0.13",
"-0.05",
"-0.05",
"-0.05",
"0.03",
"0.02",
"0.01",
"0.00",
"0.00"
] | [
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive"
] | 136 | 300 | {'C3': '70.83%', 'C1': '29.17%', 'C2': '0.0%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F2, F1, F7, F3 and F8.",
"Compare and contrast the impact of the following features (F6, F4 and F9) on the model’s prediction of C3.",
"Describe the degree of impact of the following features: F5?"
] | [
"F2",
"F1",
"F7",
"F3",
"F8",
"F6",
"F4",
"F9",
"F5"
] | {'F2': 'Checking account', 'F1': 'Duration', 'F7': 'Housing', 'F3': 'Saving accounts', 'F8': 'Sex', 'F6': 'Age', 'F4': 'Purpose', 'F9': 'Job', 'F5': 'Credit amount'} | {'F6': 'F2', 'F8': 'F1', 'F4': 'F7', 'F5': 'F3', 'F2': 'F8', 'F1': 'F6', 'F9': 'F4', 'F3': 'F9', 'F7': 'F5'} | {'C1': 'C3', 'C2': 'C1', 'C3': 'C2'} | Good Credit | {'C3': 'Good Credit', 'C1': 'Bad Credit', 'C2': 'Other'} |
SVC | C1 | Vehicle Insurance Claims | First of all, the classification decision is solely based on the information or data supplied to the prediction model. According to the model, there is a 61.61% chance that C1 is the true label, and a 38.39% chance that C2 is the true label. Since the predicted probability of C1 is higher than that of C2, it is valid to conclude that C1 is most likely the true label. The main feature responsible for this classification is F14, with a very strong positive influence, driving the model's decision higher towards C1. The next set of relevant features are F7, F17, F18, F15, F24, F32, F30, and F10. Among all the features mentioned above, F7, F18, F15, F32, and F30 have negative contributions that are responsible for the decrease in the probability that C1 is the true label. This implies that the contributions of F17, F24, and F10 combined with that of F14 explain why the model is moderately certain that C1 is the true label. | [
"0.33",
"-0.06",
"0.03",
"-0.02",
"-0.02",
"0.02",
"-0.02",
"-0.02",
"0.02",
"-0.01",
"0.01",
"-0.01",
"0.01",
"0.01",
"-0.01",
"-0.01",
"-0.01",
"-0.01",
"0.01",
"-0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 43 | 400 | {'C1': '61.61%', 'C2': '38.39%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F10, F6 and F1 (with a value equal to V2)?"
] | [
"F14",
"F7",
"F17",
"F18",
"F15",
"F24",
"F32",
"F30",
"F10",
"F6",
"F1",
"F12",
"F20",
"F27",
"F2",
"F11",
"F16",
"F28",
"F13",
"F33",
"F4",
"F3",
"F8",
"F9",
"F19",
"F31",
"F5",
"F22",
"F21",
"F26",
"F29",
"F23",
"F25"
] | {'F14': 'incident_severity', 'F7': 'insured_hobbies', 'F17': 'authorities_contacted', 'F18': 'insured_education_level', 'F15': 'umbrella_limit', 'F24': 'insured_relationship', 'F32': 'auto_make', 'F30': 'insured_occupation', 'F10': 'capital-gains', 'F6': 'policy_deductable', 'F1': 'policy_state', 'F12': 'auto_year', 'F20': 'insured_sex', 'F27': 'vehicle_claim', 'F2': 'incident_city', 'F11': 'number_of_vehicles_involved', 'F16': 'insured_zip', 'F28': 'injury_claim', 'F13': 'property_claim', 'F33': 'incident_type', 'F4': 'total_claim_amount', 'F3': 'police_report_available', 'F8': 'property_damage', 'F9': 'incident_state', 'F19': 'policy_annual_premium', 'F31': 'incident_hour_of_the_day', 'F5': 'collision_type', 'F22': 'capital-loss', 'F21': 'bodily_injuries', 'F26': 'policy_csl', 'F29': 'witnesses', 'F23': 'age', 'F25': 'months_as_customer'} | {'F27': 'F14', 'F23': 'F7', 'F28': 'F17', 'F21': 'F18', 'F5': 'F15', 'F24': 'F24', 'F33': 'F32', 'F22': 'F30', 'F7': 'F10', 'F3': 'F6', 'F18': 'F1', 'F17': 'F12', 'F20': 'F20', 'F16': 'F27', 'F30': 'F2', 'F10': 'F11', 'F6': 'F16', 'F14': 'F28', 'F15': 'F13', 'F25': 'F33', 'F13': 'F4', 'F32': 'F3', 'F31': 'F8', 'F29': 'F9', 'F4': 'F19', 'F9': 'F31', 'F26': 'F5', 'F8': 'F22', 'F11': 'F21', 'F19': 'F26', 'F12': 'F29', 'F2': 'F23', 'F1': 'F25'} | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
GradientBoostingClassifier | C1 | Paris House Classification | Because the prediction probability of C2 is barely 0.70 percent, the classifier outputs the label C1 with near 100 percent confidence based on the values of the input attributes. The effects of F8, F4, and F2 on the aforementioned classification decision are significant. The values of these features are given greater emphasis by the classifier than the others. F2 is has a negative impact among these top features, pushing the prediction judgement towards the least likely class, C2 whereas on the other hand, F8 and F4 are referred to as positive features since they improve the likelihood of the C1 label rather than the C2 label. Finally, unlike the others, the values of F1, F9, F6, and F15 have only a little influence on the label selection made here. | [
"0.37",
"-0.35",
"0.13",
"0.03",
"0.02",
"0.01",
"-0.01",
"0.01",
"0.01",
"0.00",
"0.00",
"-0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"-0.00"
] | [
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative"
] | 154 | 224 | {'C1': '99.30%', 'C2': '0.70%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F4, F13, F16 and F14) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F8",
"F2",
"F4",
"F13",
"F16",
"F14",
"F7",
"F3",
"F11",
"F12",
"F10",
"F5",
"F17",
"F1",
"F9",
"F6",
"F15"
] | {'F8': 'isNewBuilt', 'F2': 'hasYard', 'F4': 'hasPool', 'F13': 'hasStormProtector', 'F16': 'made', 'F14': 'hasGuestRoom', 'F7': 'squareMeters', 'F3': 'floors', 'F11': 'cityCode', 'F12': 'basement', 'F10': 'price', 'F5': 'numPrevOwners', 'F17': 'numberOfRooms', 'F1': 'attic', 'F9': 'cityPartRange', 'F6': 'garage', 'F15': 'hasStorageRoom'} | {'F3': 'F8', 'F1': 'F2', 'F2': 'F4', 'F4': 'F13', 'F12': 'F16', 'F16': 'F14', 'F6': 'F7', 'F8': 'F3', 'F9': 'F11', 'F13': 'F12', 'F17': 'F10', 'F11': 'F5', 'F7': 'F17', 'F14': 'F1', 'F10': 'F9', 'F15': 'F6', 'F5': 'F15'} | {'C1': 'C1', 'C2': 'C2'} | Basic | {'C1': 'Basic', 'C2': 'Luxury'} |
SGDClassifier | C3 | Flight Price-Range Classification | The classification algorithm arrived at the prediction output based on the variables or information supplied about the case under consideration. The prediction probabilities across the three-class labels, C2, C3, and C1, respectively, are 28.17%, 50.21%, and 21.62%, making C3 the label assigned by the algorithm, judged based on the prediction probabilities. The attributions analysis suggests that F4, F3, F9, and F8 are the positive features that increase the algorithm's prediction response in favour of C3. On the other hand, F7, F1, F6, F10, F2, F12, F11, and F5 have negative contributions in support of labelling the case as either C2 or C1. Overall, judging by the degree of contributions of the positive features, it is not surprising that the algorithm is moderately certain that neither C2 nor C1 is the most probable label for the case under consideration here. | [
"0.24",
"0.20",
"0.06",
"-0.06",
"0.04",
"-0.04",
"-0.04",
"-0.03",
"-0.03",
"-0.02",
"-0.01",
"-0.00"
] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 443 | 467 | {'C2': '28.17%', 'C3': '50.21%', 'C1': '21.62%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F6, F10, F2 and F12?"
] | [
"F4",
"F3",
"F9",
"F7",
"F8",
"F1",
"F6",
"F10",
"F2",
"F12",
"F11",
"F5"
] | {'F4': 'Airline', 'F3': 'Total_Stops', 'F9': 'Arrival_minute', 'F7': 'Journey_day', 'F8': 'Dep_hour', 'F1': 'Source', 'F6': 'Dep_minute', 'F10': 'Duration_hours', 'F2': 'Destination', 'F12': 'Journey_month', 'F11': 'Duration_mins', 'F5': 'Arrival_hour'} | {'F9': 'F4', 'F12': 'F3', 'F6': 'F9', 'F1': 'F7', 'F3': 'F8', 'F10': 'F1', 'F4': 'F6', 'F7': 'F10', 'F11': 'F2', 'F2': 'F12', 'F8': 'F11', 'F5': 'F5'} | {'C1': 'C2', 'C2': 'C3', 'C3': 'C1'} | Moderate | {'C2': 'Low', 'C3': 'Moderate', 'C1': 'High'} |
RandomForestClassifier | C1 | Paris House Classification | Judging based on the information provided on the case under consideration, the model outputs that the prediction probability of C2 is only 0.48%, indicating that with about 99.52% certainty, the true label here is C1 and in simple terms, the model is very confident that the true label for the case under consideration is C1. The higher degree of certainty in the above classification can be attributed solely to the positive contributions of influential features F4, F12, and F16. Analysis indicates that all the remaining features such as F1, F8, F17, F7, and F14 have moderate to low contributions towards the prediction conclusions above, whereas F3, F11, F15, and F13 are the least relevant features here. The very marginal decrease in the C1's prediction likelihood could be attributed to the influence of negative features F8, F7, F5, F13, and F11 since their contributions support labelling the case as C2 instead. Moderate positive features further driving the model to label this case as C1 are F1, F17, F10, and F14. | [
"0.32",
"0.28",
"0.07",
"0.01",
"-0.01",
"0.01",
"-0.01",
"0.01",
"0.00",
"0.00",
"0.00",
"-0.00",
"0.00",
"0.00",
"-0.00",
"0.00",
"-0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative"
] | 441 | 205 | {'C1': '99.52%', 'C2': '0.48%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F14, F10 and F9?"
] | [
"F4",
"F12",
"F16",
"F1",
"F8",
"F17",
"F7",
"F14",
"F10",
"F9",
"F2",
"F5",
"F6",
"F3",
"F11",
"F15",
"F13"
] | {'F4': 'isNewBuilt', 'F12': 'hasYard', 'F16': 'hasPool', 'F1': 'made', 'F8': 'hasStormProtector', 'F17': 'hasGuestRoom', 'F7': 'squareMeters', 'F14': 'floors', 'F10': 'price', 'F9': 'cityCode', 'F2': 'basement', 'F5': 'numPrevOwners', 'F6': 'cityPartRange', 'F3': 'numberOfRooms', 'F11': 'attic', 'F15': 'garage', 'F13': 'hasStorageRoom'} | {'F3': 'F4', 'F1': 'F12', 'F2': 'F16', 'F12': 'F1', 'F4': 'F8', 'F16': 'F17', 'F6': 'F7', 'F8': 'F14', 'F17': 'F10', 'F9': 'F9', 'F13': 'F2', 'F11': 'F5', 'F10': 'F6', 'F7': 'F3', 'F14': 'F11', 'F15': 'F15', 'F5': 'F13'} | {'C1': 'C1', 'C2': 'C2'} | Basic | {'C1': 'Basic', 'C2': 'Luxury'} |
GradientBoostingClassifier | C2 | Basketball Players Career Length Prediction | Judging based on the values of the variables passed to the model with respect to the case under consideration, the output labelling decision is as follows: there is about an 83.98% chance that C2 is the correct label, whereas the likelihood of C1 is only 16.02%, hence the label choice with a higher confidence level is C2. The top-variables influencing this decision are F2, F4, F3, and F14, while the least important variables are F9, F12, and F13. According to the variable contributions analysis performed, only the input variables F8, F16, F19, and F15 exhibit negative attributions, pushing the prediction decision towards the alternative label, C1. The other variables positively support the C2 prediction, shifting the verdict strongly away from the C1 class. In conclusion, positive variables such as F2, F4, F3, F14, F7, and F17 have a higher joint contribution compared to the negative features, which can explain why the model is certain that C2 is the most probable label. | [
"0.12",
"0.07",
"0.05",
"0.05",
"0.04",
"-0.04",
"0.03",
"0.02",
"0.02",
"0.01",
"-0.01",
"-0.01",
"0.01",
"0.00",
"-0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 11 | 367 | {'C2': '83.98%', 'C1': '16.02%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F3, F14, F11 and F8) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F2",
"F4",
"F3",
"F14",
"F11",
"F8",
"F17",
"F7",
"F10",
"F6",
"F16",
"F19",
"F18",
"F1",
"F15",
"F5",
"F9",
"F13",
"F12"
] | {'F2': 'GamesPlayed', 'F4': 'OffensiveRebounds', 'F3': 'FreeThrowPercent', 'F14': 'FieldGoalPercent', 'F11': '3PointPercent', 'F8': '3PointAttempt', 'F17': 'FieldGoalsMade', 'F7': 'Blocks', 'F10': 'DefensiveRebounds', 'F6': 'Turnovers', 'F16': 'Rebounds', 'F19': 'MinutesPlayed', 'F18': 'FreeThrowAttempt', 'F1': 'Assists', 'F15': '3PointMade', 'F5': 'FieldGoalsAttempt', 'F9': 'PointsPerGame', 'F13': 'Steals', 'F12': 'FreeThrowMade'} | {'F1': 'F2', 'F13': 'F4', 'F12': 'F3', 'F6': 'F14', 'F9': 'F11', 'F8': 'F8', 'F4': 'F17', 'F18': 'F7', 'F14': 'F10', 'F19': 'F6', 'F15': 'F16', 'F2': 'F19', 'F11': 'F18', 'F16': 'F1', 'F7': 'F15', 'F5': 'F5', 'F3': 'F9', 'F17': 'F13', 'F10': 'F12'} | {'C1': 'C2', 'C2': 'C1'} | More than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
RandomForestClassifier | C4 | Mobile Price-Range Classification | The model predicts the class label C4 for the given test instance with a likelihood of about 69.23%. However, there is about a 30.77% chance that the true class label is C2, while the others, C3 and C1, have a 0.0% likelihood. The top features contributing to this prediction decision are F1, F15, F9, and F2, whereas the least important are F12, F11, and F6. Among the top features, while F1 and F15 have values that shift the prediction decision towards the C4 class label, the values of F9 and F2 suggest that the true label could likely be C2. For the features with moderate influence on the decision, F4, F14, F18, and F5 have negative contributions, further decreasing the confidence level in the C4 assignment. On the other hand, the moderate positive influences of F17, F8, F20, F19, and F16 drive the decision further towards the C4 label. Considering the attributions of the input features, it is surprising that the confidence level is just 69.23% since the top feature, F1, has the highest contribution among all the input features. Finally, the values of F10, F11, and F6, though shown to be less important when deciding the correct label for the given case, have positive contributions to the prediction with respect to the given case. | [
"0.50",
"0.04",
"-0.03",
"-0.02",
"0.02",
"0.02",
"-0.01",
"0.01",
"0.01",
"0.01",
"-0.01",
"-0.01",
"-0.01",
"-0.01",
"0.01",
"-0.01",
"0.01",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive"
] | 76 | 424 | {'C3': '0.00%', 'C4': '69.23%', 'C2': '30.77%', 'C1': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F8, F4 (value equal to V0) and F19) with moderate impact on the prediction made for this test case."
] | [
"F1",
"F15",
"F9",
"F2",
"F17",
"F8",
"F4",
"F19",
"F20",
"F16",
"F14",
"F18",
"F5",
"F7",
"F3",
"F13",
"F10",
"F12",
"F11",
"F6"
] | {'F1': 'ram', 'F15': 'touch_screen', 'F9': 'int_memory', 'F2': 'battery_power', 'F17': 'mobile_wt', 'F8': 'sc_w', 'F4': 'four_g', 'F19': 'talk_time', 'F20': 'sc_h', 'F16': 'wifi', 'F14': 'fc', 'F18': 'three_g', 'F5': 'dual_sim', 'F7': 'n_cores', 'F3': 'px_height', 'F13': 'blue', 'F10': 'clock_speed', 'F12': 'px_width', 'F11': 'm_dep', 'F6': 'pc'} | {'F11': 'F1', 'F19': 'F15', 'F4': 'F9', 'F1': 'F2', 'F6': 'F17', 'F13': 'F8', 'F17': 'F4', 'F14': 'F19', 'F12': 'F20', 'F20': 'F16', 'F3': 'F14', 'F18': 'F18', 'F16': 'F5', 'F7': 'F7', 'F9': 'F3', 'F15': 'F13', 'F2': 'F10', 'F10': 'F12', 'F5': 'F11', 'F8': 'F6'} | {'C1': 'C3', 'C2': 'C4', 'C3': 'C2', 'C4': 'C1'} | r2 | {'C3': 'r1', 'C4': 'r2', 'C2': 'r3', 'C1': 'r4'} |
KNeighborsClassifier | C2 | Water Quality Classification | The given case is likely C2 with a confidence level of 87.50% judged based on the values of the input features supplied to the classifier and according to the attributions analysis, F9 and F2 have a high degree of impact. F6, F8, F3, F4, and F5 have a moderate degree of impact while on the contrary F7 and F1 have little impact. Examining further, the values of F9, F2, F6, and F8 all have a positive influence on the classifier supporting the label assignment decision for the given test case. F3 and F5 are also positively supporting features, whereas F4 has a negative influence on the final classification. Finally, F7 and F1 both have very little contributions, though F1 has significantly less than even F7. | [
"0.03",
"0.01",
"0.01",
"0.01",
"0.01",
"0.01",
"-0.00",
"-0.00",
"-0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative"
] | 51 | 19 | {'C2': '87.50%', 'C1': '12.50%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F9, F2, F6 and F8) on the prediction made for this test case.",
"Compare the direction of impact of the features: F3, F5 and F4.",
"Describe the degree of impact of the following features: F7 and F1?"
] | [
"F9",
"F2",
"F6",
"F8",
"F3",
"F5",
"F4",
"F7",
"F1"
] | {'F9': 'Hardness', 'F2': 'Sulfate', 'F6': 'Solids', 'F8': 'ph', 'F3': 'Organic_carbon', 'F5': 'Conductivity', 'F4': 'Trihalomethanes', 'F7': 'Turbidity', 'F1': 'Chloramines'} | {'F2': 'F9', 'F5': 'F2', 'F3': 'F6', 'F1': 'F8', 'F7': 'F3', 'F6': 'F5', 'F8': 'F4', 'F9': 'F7', 'F4': 'F1'} | {'C1': 'C2', 'C2': 'C1'} | Not Portable | {'C2': 'Not Portable', 'C1': 'Portable'} |
RandomForestClassifier | C2 | Mobile Price-Range Classification | The label for this example is estimated to be C2 among the four possible classes, with a 73.08 percent chance of being true. C1 is the next most likely label, with a probability of roughly 26.92 percent. The above prediction assessment is mostly dependent on the values of the variables F3, F9, F8, F5, and F12. F3 had the greatest influence, followed by F8, F9, F12, and F5. The positive variables F3, F9, F10, and F17 outnumber the negative variables F8, F12, F5, and F19. Twelve of the twenty variables have values that tilt the prediction towards one of the three other probable classifications. As a result, it is not unexpected that the model is not completely certain of the C2 assigned. Given that the chance of C2's being accurate is 73.08 percent, the model appears to be relatively confident in its final judgement for the data instance under review. | [
"0.78",
"-0.07",
"0.06",
"-0.06",
"-0.02",
"0.02",
"0.02",
"-0.02",
"-0.01",
"-0.01",
"-0.01",
"-0.01",
"0.01",
"-0.01",
"-0.01",
"0.01",
"0.00",
"-0.00",
"-0.00",
"0.00"
] | [
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive"
] | 130 | 305 | {'C2': '73.08%', 'C1': '26.92%', 'C4': '0.00%', 'C3': '0.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F17, F19, F11 and F14?"
] | [
"F3",
"F8",
"F9",
"F12",
"F5",
"F10",
"F17",
"F19",
"F11",
"F14",
"F15",
"F20",
"F6",
"F18",
"F4",
"F7",
"F1",
"F16",
"F13",
"F2"
] | {'F3': 'ram', 'F8': 'px_width', 'F9': 'battery_power', 'F12': 'px_height', 'F5': 'n_cores', 'F10': 'dual_sim', 'F17': 'touch_screen', 'F19': 'int_memory', 'F11': 'wifi', 'F14': 'fc', 'F15': 'four_g', 'F20': 'm_dep', 'F6': 'pc', 'F18': 'mobile_wt', 'F4': 'talk_time', 'F7': 'three_g', 'F1': 'sc_h', 'F16': 'sc_w', 'F13': 'blue', 'F2': 'clock_speed'} | {'F11': 'F3', 'F10': 'F8', 'F1': 'F9', 'F9': 'F12', 'F7': 'F5', 'F16': 'F10', 'F19': 'F17', 'F4': 'F19', 'F20': 'F11', 'F3': 'F14', 'F17': 'F15', 'F5': 'F20', 'F8': 'F6', 'F6': 'F18', 'F14': 'F4', 'F18': 'F7', 'F12': 'F1', 'F13': 'F16', 'F15': 'F13', 'F2': 'F2'} | {'C1': 'C2', 'C2': 'C1', 'C3': 'C4', 'C4': 'C3'} | r1 | {'C2': 'r1', 'C1': 'r2', 'C4': 'r3', 'C3': 'r4'} |
BernoulliNB | C2 | Personal Loan Modelling | The model has classified the instance as C2 due to the effects of the following features: F5, F8, F6, and F2. Based on the values of these variables, the likelihood of the C2 label is 65.51 percent. F2 and F6 are the top positively contributing variables, whereas F5 and F8 are the most adversely contributing variables. Unlike F2 and F6, which have greater influences on the model's prediction choice in this situation, F3 and F9 have fairly modest positive influences. Finally, F1, F7, and F4 show negative predictive effects, however, as compared to F5, their attributions are modest. | [
"0.34",
"0.08",
"-0.03",
"-0.02",
"0.02",
"0.01",
"-0.01",
"-0.01",
"-0.01"
] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative"
] | 135 | 296 | {'C1': '34.49%', 'C2': '65.51%'} | [
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Summarize the direction of influence of the features (F2, F6 and F5) on the prediction made for this test case.",
"Compare the direction of impact of the features: F8, F3 and F9.",
"Describe the degree of impact of the following features: F1, F7 and F4?"
] | [
"F2",
"F6",
"F5",
"F8",
"F3",
"F9",
"F1",
"F7",
"F4"
] | {'F2': 'CD Account', 'F6': 'Income', 'F5': 'CCAvg', 'F8': 'Securities Account', 'F3': 'Education', 'F9': 'Mortgage', 'F1': 'Age', 'F7': 'Family', 'F4': 'Extra_service'} | {'F8': 'F2', 'F2': 'F6', 'F4': 'F5', 'F7': 'F8', 'F5': 'F3', 'F6': 'F9', 'F1': 'F1', 'F3': 'F7', 'F9': 'F4'} | {'C1': 'C1', 'C2': 'C2'} | Accept | {'C1': 'Reject', 'C2': 'Accept'} |
DecisionTreeClassifier | C2 | Insurance Churn | Considering the predicted likelihoods across the classes, C2 is confidently chosen as the true label since its likelihood is 93.27%, implying that the likelihood of C1 is only about 6.73%. F6 and F15 are the two features with a very strong positive influence, favouring the prediction of class C2. The following features have a moderate effect and are listed in descending order of influence: F5 and F10 have a negative effect, while F4 and F13 have a positive effect on the prediction of C2. Similar to F5 and F10, the features F9 and F8 also negatively affected the prediction decision. Finally, the values of F2, F16, F1, and F7 are the least important to the model decision for this case. | [
"0.38",
"0.21",
"-0.05",
"-0.04",
"0.04",
"0.04",
"-0.02",
"-0.02",
"-0.02",
"-0.02",
"0.01",
"-0.01",
"0.01",
"-0.00",
"0.00",
"-0.00"
] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 83 | 284 | {'C1': '6.73%', 'C2': '93.27%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F13 (equal to V0), F9 and F8) with moderate impact on the prediction made for this test case."
] | [
"F6",
"F15",
"F5",
"F10",
"F4",
"F13",
"F9",
"F8",
"F12",
"F11",
"F3",
"F14",
"F2",
"F16",
"F1",
"F7"
] | {'F6': 'feature15', 'F15': 'feature14', 'F5': 'feature10', 'F10': 'feature11', 'F4': 'feature5', 'F13': 'feature13', 'F9': 'feature4', 'F8': 'feature3', 'F12': 'feature12', 'F11': 'feature1', 'F3': 'feature7', 'F14': 'feature2', 'F2': 'feature6', 'F16': 'feature0', 'F1': 'feature9', 'F7': 'feature8'} | {'F9': 'F6', 'F8': 'F15', 'F4': 'F5', 'F5': 'F10', 'F15': 'F4', 'F7': 'F13', 'F14': 'F9', 'F13': 'F8', 'F6': 'F12', 'F11': 'F11', 'F1': 'F3', 'F12': 'F14', 'F16': 'F2', 'F10': 'F16', 'F3': 'F1', 'F2': 'F7'} | {'C1': 'C1', 'C2': 'C2'} | Leave | {'C1': 'Stay', 'C2': 'Leave'} |
GradientBoostingClassifier | C1 | Basketball Players Career Length Prediction | The classification output is C1, however, the classifier is somewhat unsure about this prediction decision because the corresponding predicted probability is only 55.19%. F11 is by far the most influential feature whereas F4, F6, and F17 have been recognised as having the biggest effect on prediction output here after F11. The combination of F11, F4, F6, F17, and F3 features has resulted in the classification choice being altered from C1 to C2. While F5, F2, and F16 all have a minor influence on the classification, F5 is the only one that has a positive impact on the C1 classification. In this case, many features had lower influence on the prediction, with F15, F8, F13, F19, and F10 having a marginal effect. | [
"-0.12",
"-0.07",
"-0.05",
"-0.05",
"-0.04",
"0.04",
"-0.03",
"-0.02",
"-0.02",
"-0.01",
"0.01",
"0.01",
"-0.01",
"0.00",
"-0.00",
"-0.00",
"-0.00",
"-0.00",
"-0.00"
] | [
"negative",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 88 | 268 | {'C2': '44.81%', 'C1': '55.19%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F11, F4, F6, F17 and F3.",
"Summarize the direction of influence of the features (F5, F2 and F16) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F11",
"F4",
"F6",
"F17",
"F3",
"F5",
"F2",
"F16",
"F9",
"F14",
"F12",
"F1",
"F7",
"F18",
"F15",
"F8",
"F13",
"F19",
"F10"
] | {'F11': 'GamesPlayed', 'F4': 'OffensiveRebounds', 'F6': 'FieldGoalPercent', 'F17': 'FreeThrowPercent', 'F3': '3PointPercent', 'F5': '3PointAttempt', 'F2': 'FieldGoalsMade', 'F16': 'Blocks', 'F9': 'DefensiveRebounds', 'F14': 'Turnovers', 'F12': 'Rebounds', 'F1': 'MinutesPlayed', 'F7': 'FreeThrowAttempt', 'F18': '3PointMade', 'F15': 'Assists', 'F8': 'PointsPerGame', 'F13': 'FreeThrowMade', 'F19': 'FieldGoalsAttempt', 'F10': 'Steals'} | {'F1': 'F11', 'F13': 'F4', 'F6': 'F6', 'F12': 'F17', 'F9': 'F3', 'F8': 'F5', 'F4': 'F2', 'F18': 'F16', 'F14': 'F9', 'F19': 'F14', 'F15': 'F12', 'F2': 'F1', 'F11': 'F7', 'F7': 'F18', 'F16': 'F15', 'F3': 'F8', 'F10': 'F13', 'F5': 'F19', 'F17': 'F10'} | {'C1': 'C2', 'C2': 'C1'} | Less than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
LogisticRegression | C2 | Customer Churn Modelling | Judging based on the values of the input variables, the classification algorithm labels the case as C2 since its prediction likelihood is equal to 88.69%. The prediction decision is primarily based on the contributions of F2, F1, and F9, however, F7, F4, and F10 are shown to be the least important variables. Regarding the direction of influence of the variables, F2, F9, F6, F7, and F4 are the positive variables that increase the odds of C2 being the correct label. Driving the prediction toward the alternative label, C1, are the variables F1, F3, F8, F5, and F10. Owing to the fact that the most influential variables, F2 and F9, have strong positive attributions, outweighing the contributions of the negative variables, it is not surprising that the algorithm is certain about the decision made. | [
"0.15",
"0.14",
"-0.11",
"-0.07",
"-0.02",
"-0.02",
"0.01",
"0.01",
"0.00",
"-0.00"
] | [
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative"
] | 335 | 188 | {'C2': '88.69%', 'C1': '11.31%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F6, F7 and F4?"
] | [
"F2",
"F9",
"F1",
"F3",
"F8",
"F5",
"F6",
"F7",
"F4",
"F10"
] | {'F2': 'IsActiveMember', 'F9': 'NumOfProducts', 'F1': 'Geography', 'F3': 'Gender', 'F8': 'Age', 'F5': 'CreditScore', 'F6': 'EstimatedSalary', 'F7': 'Balance', 'F4': 'Tenure', 'F10': 'HasCrCard'} | {'F9': 'F2', 'F7': 'F9', 'F2': 'F1', 'F3': 'F3', 'F4': 'F8', 'F1': 'F5', 'F10': 'F6', 'F6': 'F7', 'F5': 'F4', 'F8': 'F10'} | {'C1': 'C2', 'C2': 'C1'} | Stay | {'C2': 'Stay', 'C1': 'Leave'} |
BernoulliNB | C1 | Water Quality Classification | The classification algorithm predicts class C1 with a confidence level of 61.55% and this implies that the probability of the alternative label is only 38.45%. In this case, the top features driving the prediction decision are F7, F9, F1, and F2, followed by F4, F5, F8, F3, and finally F6. Based on the inspections performed to understand the direction of influence of the input features, it can be concluded that F7 has the strongest positive contribution, while F1 has the strongest negative contribution and conversely, all the remaining features have moderate contributions. The other positive features are F9, F4, F5, and F3, whereas the remaining negatives are F2, F8, and F6. All things considered, the influence of the negative features indicates that the likelihood of the C2 label is 38.45% while the positive contributions push the prediction higher towards C1 resulting in the 61.55% prediction confidence. | [
"0.09",
"0.06",
"-0.03",
"-0.01",
"0.01",
"0.01",
"-0.00",
"0.00",
"-0.00"
] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative"
] | 101 | 417 | {'C1': '61.55%', 'C2': '38.45%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F2, F4 and F5) on the model’s prediction of C1.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F7",
"F9",
"F1",
"F2",
"F4",
"F5",
"F8",
"F3",
"F6"
] | {'F7': 'Sulfate', 'F9': 'ph', 'F1': 'Trihalomethanes', 'F2': 'Chloramines', 'F4': 'Organic_carbon', 'F5': 'Hardness', 'F8': 'Solids', 'F3': 'Turbidity', 'F6': 'Conductivity'} | {'F5': 'F7', 'F1': 'F9', 'F8': 'F1', 'F4': 'F2', 'F7': 'F4', 'F2': 'F5', 'F3': 'F8', 'F9': 'F3', 'F6': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | Not Portable | {'C1': 'Not Portable', 'C2': 'Portable'} |
RandomForestClassifier | C2 | Flight Price-Range Classification | The classification model's decision about the true label for the case is based on the information provided to it. Among the three labels, C2, C3, and C1, the model shows without a doubt that neither C3 nor C1 is the true label, given that the probability of C2 being the true label is 100.0%. F12, F6, and F2 are the main contributing factors or variables in the final verdict here since their respective influence outranks the remaining variables. In fact, analysis indicates that F3, F11, and F5 are the least influential variables since they receive little emphasis from the model when making the labelling decision here. In between F12, F6, and F2, and F3, F11, F8, and F5, are the variables such as F9, F10, F1, and F7 with moderate influence on the classification decision here. Among the variables passed to the model, only F9, F7, and F3 are shown to have negative contributions, which suggests that perhaps the true label could be either of the remaining labels. However, given the 100.0% predicted likelihood of C2, it is reasonable to deduce that the positive variables, such as F12, F6, F2, F10, F4, and F1, significantly influence the model's judgement towards C2. | [
"0.23",
"0.19",
"0.17",
"-0.05",
"0.03",
"0.02",
"-0.02",
"0.02",
"0.01",
"-0.01",
"0.01",
"0.01"
] | [
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive"
] | 436 | 203 | {'C2': '100.00%', 'C3': '0.00%', 'C1': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F1, F7 and F4) with moderate impact on the prediction made for this test case."
] | [
"F12",
"F6",
"F2",
"F9",
"F10",
"F1",
"F7",
"F4",
"F8",
"F3",
"F11",
"F5"
] | {'F12': 'Duration_hours', 'F6': 'Airline', 'F2': 'Total_Stops', 'F9': 'Journey_day', 'F10': 'Source', 'F1': 'Duration_mins', 'F7': 'Arrival_hour', 'F4': 'Destination', 'F8': 'Arrival_minute', 'F3': 'Dep_minute', 'F11': 'Journey_month', 'F5': 'Dep_hour'} | {'F7': 'F12', 'F9': 'F6', 'F12': 'F2', 'F1': 'F9', 'F10': 'F10', 'F8': 'F1', 'F5': 'F7', 'F11': 'F4', 'F6': 'F8', 'F4': 'F3', 'F2': 'F11', 'F3': 'F5'} | {'C1': 'C2', 'C2': 'C3', 'C3': 'C1'} | Low | {'C2': 'Low', 'C3': 'Moderate', 'C1': 'High'} |
LogisticRegression | C1 | Basketball Players Career Length Prediction | According to the model, C1 is the class with the higher probability, which is equal to 52.57 percent, of being the label for this selected instance or case. Conversely, there is a 47.43 percent chance that C2 is the correct label showing that the model is less certain about the classification verdict in this case. This uncertainty can be linked to the fact that the majority of variables have values that favour assigning C2. The only variables increasing the model's response to prediction C1 are the positive variables namely: F6, F16, F7, F1, F8, F11, and F5. The top negative variables decreasing the likelihood of C1 are F14 and F19 supported by other negative variables, F2, F18, and F9, that further shift the verdict towards C2. | [
"-0.18",
"0.18",
"-0.10",
"0.08",
"-0.08",
"-0.07",
"-0.06",
"0.06",
"-0.04",
"0.04",
"0.04",
"-0.03",
"-0.03",
"-0.02",
"0.02",
"-0.01",
"-0.01",
"0.01",
"-0.01"
] | [
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative"
] | 165 | 91 | {'C2': '47.43%', 'C1': '52.57%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F16, F2 and F18) with moderate impact on the prediction made for this test case."
] | [
"F14",
"F6",
"F19",
"F16",
"F2",
"F18",
"F9",
"F7",
"F12",
"F1",
"F8",
"F15",
"F10",
"F17",
"F11",
"F3",
"F4",
"F5",
"F13"
] | {'F14': '3PointMade', 'F6': '3PointAttempt', 'F19': 'FreeThrowMade', 'F16': 'FreeThrowAttempt', 'F2': 'GamesPlayed', 'F18': 'OffensiveRebounds', 'F9': 'FieldGoalsAttempt', 'F7': 'DefensiveRebounds', 'F12': 'Assists', 'F1': 'MinutesPlayed', 'F8': 'FieldGoalsMade', 'F15': 'Blocks', 'F10': 'Rebounds', 'F17': 'FieldGoalPercent', 'F11': 'Steals', 'F3': 'PointsPerGame', 'F4': 'FreeThrowPercent', 'F5': 'Turnovers', 'F13': '3PointPercent'} | {'F7': 'F14', 'F8': 'F6', 'F10': 'F19', 'F11': 'F16', 'F1': 'F2', 'F13': 'F18', 'F5': 'F9', 'F14': 'F7', 'F16': 'F12', 'F2': 'F1', 'F4': 'F8', 'F18': 'F15', 'F15': 'F10', 'F6': 'F17', 'F17': 'F11', 'F3': 'F3', 'F12': 'F4', 'F19': 'F5', 'F9': 'F13'} | {'C1': 'C2', 'C2': 'C1'} | Less than 5 | {'C2': 'More than 5', 'C1': 'Less than 5'} |
RandomForestClassifier | C2 | Printer Sales | According to the predicted likelihoods across the classes, C1 has a 17.0% chance of being the true label for the given data or case, implying that C2 is the most likely label. F12, F19, and F20 are the most important factors that led to the classification judgments above. The remaining factors have a minor or non-existent impact on the classifier. The classifier most likely ignored the values of F18, F3, F1, F8, F14, and F5 when giving a label to this case since their relative degrees of impact are extremely near to zero. F9, F15, F24, F11, F21, and F4 are considered negative factors among the significant factors because their contributions to the choice tend to reduce the chance that C2 is the correct label. These negatives features lend themselves to the case being classified as C1 but the remaining features contribute positively, raising the likelihood of the C2 classification. | [
"0.10",
"0.07",
"0.06",
"0.06",
"0.03",
"0.03",
"-0.02",
"0.02",
"0.02",
"-0.02",
"-0.02",
"0.02",
"0.02",
"0.01",
"-0.01",
"-0.01",
"-0.01",
"0.01",
"0.01",
"0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 240 | 322 | {'C2': '83.00%', 'C1': '17.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F9, F7 and F23?"
] | [
"F12",
"F19",
"F20",
"F26",
"F10",
"F22",
"F9",
"F7",
"F23",
"F15",
"F24",
"F16",
"F6",
"F25",
"F11",
"F21",
"F4",
"F13",
"F2",
"F17",
"F18",
"F3",
"F1",
"F8",
"F14",
"F5"
] | {'F12': 'X8', 'F19': 'X24', 'F20': 'X1', 'F26': 'X2', 'F10': 'X10', 'F22': 'X15', 'F9': 'X25', 'F7': 'X23', 'F23': 'X18', 'F15': 'X4', 'F24': 'X7', 'F16': 'X17', 'F6': 'X3', 'F25': 'X22', 'F11': 'X5', 'F21': 'X9', 'F4': 'X12', 'F13': 'X19', 'F2': 'X11', 'F17': 'X16', 'F18': 'X14', 'F3': 'X21', 'F1': 'X20', 'F8': 'X13', 'F14': 'X6', 'F5': 'X26'} | {'F8': 'F12', 'F24': 'F19', 'F1': 'F20', 'F2': 'F26', 'F10': 'F10', 'F15': 'F22', 'F25': 'F9', 'F23': 'F7', 'F18': 'F23', 'F4': 'F15', 'F7': 'F24', 'F17': 'F16', 'F3': 'F6', 'F22': 'F25', 'F5': 'F11', 'F9': 'F21', 'F12': 'F4', 'F19': 'F13', 'F11': 'F2', 'F16': 'F17', 'F14': 'F18', 'F21': 'F3', 'F20': 'F1', 'F13': 'F8', 'F6': 'F14', 'F26': 'F5'} | {'C1': 'C2', 'C2': 'C1'} | Less | {'C2': 'Less', 'C1': 'More'} |
RandomForestClassifier | C1 | Credit Risk Classification | According to the ML model, C1 is the most likely class label, and we can conclude that the model is quite confident about the decision given that the probability of having C2 as the correct label is only 7.0%. For the case under study, analysis indicates that F4, F8, F3, and F6 are essentially the negative set of features that push the forecast higher towards C2 instead of C1, while F7, F11, F2, and F1 increase the odds of the prediction being equal to C1. In general, the most relevant feature is F7, while F5 and F9 are the least relevant features, with marginal influence on the above classification verdict. In summary, given the very strong positive influence of F7 together with the moderate influence of the other positives, F11, F1, and F2, it is not strange that the model chose to label the case as C1 instead of C2. | [
"0.10",
"-0.02",
"0.01",
"-0.01",
"0.01",
"0.01",
"-0.00",
"-0.00",
"-0.00",
"-0.00",
"-0.00"
] | [
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative"
] | 182 | 287 | {'C1': '93.00%', 'C2': '7.00%'} | [
"In a single sentence, state the prediction output of the model for the selected test case along with the confidence level of the prediction (if applicable).",
"In no less three sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Describe the degree of impact of the following features: F10, F9 and F5?"
] | [
"F7",
"F3",
"F11",
"F4",
"F1",
"F2",
"F8",
"F6",
"F10",
"F9",
"F5"
] | {'F7': 'fea_4', 'F3': 'fea_10', 'F11': 'fea_8', 'F4': 'fea_7', 'F1': 'fea_2', 'F2': 'fea_3', 'F8': 'fea_5', 'F6': 'fea_1', 'F10': 'fea_9', 'F9': 'fea_6', 'F5': 'fea_11'} | {'F4': 'F7', 'F10': 'F3', 'F8': 'F11', 'F7': 'F4', 'F2': 'F1', 'F3': 'F2', 'F5': 'F8', 'F1': 'F6', 'F9': 'F10', 'F6': 'F9', 'F11': 'F5'} | {'C1': 'C1', 'C2': 'C2'} | Low | {'C1': 'Low', 'C2': 'High'} |
MLPClassifier | C2 | Annual Income Earnings | Because the confidence level associated with the other class, C1, is just 2.29%, the model predicts that the given example is likely C2 and to be specific, the model is quite certain that the right label for the given case is C2. All the features are shown to have some degree of influence on the decision above, with F14 and F6 being the least relevant features, while F5 and F9 are the top features. From the analysis performed to understand how each feature contributes to the above prediction assertion, only the features F13, F12, F1, F8, F7, and F6, have negative influences, shifting the prediction verdict towards C1. The remaining features all contribute positively, strongly shifting the prediction towards the assigned label which could explain the prediction confidence level associated with label C2. The most positive features are F9, F2, and F5 with stronger push in favour of the output label and they are supported by other positive features such as F4, F10, F3, and F11 have a moderate degree of influence. | [
"0.62",
"0.24",
"-0.14",
"0.09",
"-0.08",
"0.08",
"0.06",
"0.06",
"0.05",
"-0.02",
"-0.02",
"-0.02",
"0.01",
"-0.00"
] | [
"positive",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"positive",
"negative",
"negative",
"negative",
"positive",
"negative"
] | 201 | 116 | {'C1': '2.29%', 'C2': '97.71%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F5, F9, F13, F2 and F12.",
"Compare and contrast the impact of the following features (F3, F11 and F4) on the model’s prediction of C2.",
"Describe the degree of impact of the following features: F10, F8 and F1?"
] | [
"F5",
"F9",
"F13",
"F2",
"F12",
"F3",
"F11",
"F4",
"F10",
"F8",
"F1",
"F7",
"F14",
"F6"
] | {'F5': 'Capital Gain', 'F9': 'Marital Status', 'F13': 'Capital Loss', 'F2': 'Relationship', 'F12': 'Hours per week', 'F3': 'Education', 'F11': 'Country', 'F4': 'Age', 'F10': 'Occupation', 'F8': 'Sex', 'F1': 'Education-Num', 'F7': 'Workclass', 'F14': 'fnlwgt', 'F6': 'Race'} | {'F11': 'F5', 'F6': 'F9', 'F12': 'F13', 'F8': 'F2', 'F13': 'F12', 'F4': 'F3', 'F14': 'F11', 'F1': 'F4', 'F7': 'F10', 'F10': 'F8', 'F5': 'F1', 'F2': 'F7', 'F3': 'F14', 'F9': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | Above 50K | {'C1': 'Under 50K', 'C2': 'Above 50K'} |
KNNClassifier | C1 | Car Acceptability Valuation | The classifier made the prediction here based on the information provided about the case under consideration, and according to the classifier, the prediction probabilities or likelihoods across the labels C1 and C2 are 100.0% and 0.0%, respectively. All the input features are shown to have different degrees of influence on the final decision here by the classifier. The most influential features are F5 and F3, with F2 and F6 ranked as the least contributing factors. The values of F4 and F1 suggest that perhaps the true label could be C2 since they are the negative features. However, considering the confidence in C1, it is valid to conclude that the joint influence or contribution to the classification of the negative features with respect to the given case is outmatched by the joint positive attribution of F5, F3, F2, and F6. | [
"0.34",
"0.33",
"-0.13",
"-0.12",
"0.06",
"0.04"
] | [
"positive",
"positive",
"negative",
"negative",
"positive",
"positive"
] | 435 | 462 | {'C1': '100.00%', 'C2': '0.00%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F1, F4, F2 and F6) with moderate impact on the prediction made for this test case."
] | [
"F5",
"F3",
"F1",
"F4",
"F2",
"F6"
] | {'F5': 'persons', 'F3': 'safety', 'F1': 'lug_boot', 'F4': 'buying', 'F2': 'doors', 'F6': 'maint'} | {'F4': 'F5', 'F6': 'F3', 'F5': 'F1', 'F1': 'F4', 'F3': 'F2', 'F2': 'F6'} | {'C1': 'C1', 'C2': 'C2'} | Unacceptable | {'C1': 'Unacceptable', 'C2': 'Acceptable'} |
LogisticRegression | C1 | Real Estate Investment | For the selected case, the model assigns the label C1. The prediction probability distribution across the classes C2 and C1 is 2.40% and 97.60%, respectively. The most important features considered for this prediction are F18, F3, F12, and F15, while on the other hand, the least relevant features with little contributions to the decision based on the analysis are F1, F8, F19, and F9. The top positive features Increasing the likelihood of the prediction being made are F18, F3, and F15. Pushing the prediction towards the alternative class C2, the top negative features are F12, F14, and F4. F17, F11, F13, F7, and F2 are some of the features that have a moderate impact on the classification decision in this case. | [
"0.45",
"0.25",
"-0.12",
"0.11",
"-0.03",
"-0.03",
"0.03",
"-0.03",
"-0.02",
"0.02",
"-0.01",
"-0.01",
"0.01",
"-0.01",
"0.01",
"-0.01",
"0.00",
"0.00",
"0.00",
"-0.00"
] | [
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative"
] | 159 | 86 | {'C2': '2.40%', 'C1': '97.60%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F18, F3 and F12.",
"Summarize the direction of influence of the features (F15, F14 and F4) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F18",
"F3",
"F12",
"F15",
"F14",
"F4",
"F17",
"F11",
"F13",
"F2",
"F7",
"F20",
"F6",
"F10",
"F16",
"F5",
"F1",
"F8",
"F19",
"F9"
] | {'F18': 'Feature7', 'F3': 'Feature4', 'F12': 'Feature2', 'F15': 'Feature14', 'F14': 'Feature15', 'F4': 'Feature8', 'F17': 'Feature20', 'F11': 'Feature1', 'F13': 'Feature17', 'F2': 'Feature3', 'F7': 'Feature16', 'F20': 'Feature18', 'F6': 'Feature10', 'F10': 'Feature5', 'F16': 'Feature6', 'F5': 'Feature12', 'F1': 'Feature19', 'F8': 'Feature13', 'F19': 'Feature9', 'F9': 'Feature11'} | {'F11': 'F18', 'F9': 'F3', 'F1': 'F12', 'F17': 'F15', 'F4': 'F14', 'F3': 'F4', 'F20': 'F17', 'F7': 'F11', 'F6': 'F13', 'F8': 'F2', 'F18': 'F7', 'F19': 'F20', 'F13': 'F6', 'F2': 'F10', 'F10': 'F16', 'F15': 'F5', 'F5': 'F1', 'F16': 'F8', 'F12': 'F19', 'F14': 'F9'} | {'C1': 'C2', 'C2': 'C1'} | Invest | {'C2': 'Ignore', 'C1': 'Invest'} |
MLPClassifier | C1 | Vehicle Insurance Claims | The given instance was labelled as C1 by the model based on the values of its features. The model is about 79.64% certain about this prediction decision, hence, there is a slight chance that the label could be C2. Among the different features, the ones with the most impact on the model are F8, F25, F9, F2, and F27. The most negative feature is F8, and it is significantly pushing the narrative toward the prediction of C2. From this, it is foreseeable that there is a chance that the true label could be C2 which is about 20.36%. The influence of F8 and F9 is somewhat counterbalanced by the values of the features F25, F2, and F27. Other attributes that shift the decision in favour of C2 are F7 and F24. F3 shifts the decision further in the direction of C1 and in addition, F31 supports the model's prediction while the values of F5 and F32 of the given test case contradict the model's decision, decreasing the likelihood of C1. Among the features not relevant to this prediction decision for this case are F10, F20, F28, and F26. | [
"-0.47",
"0.11",
"-0.08",
"0.07",
"0.07",
"-0.07",
"-0.06",
"0.06",
"0.05",
"-0.04",
"-0.03",
"0.03",
"-0.03",
"0.03",
"-0.03",
"0.02",
"0.02",
"-0.02",
"-0.02",
"0.02",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"positive",
"positive",
"negative",
"negative",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 78 | 28 | {'C1': '79.64%', 'C2': '20.36%'} | [
"Provide a statement summarizing the prediction made for the test case.",
"For the current test instance, describe the direction of influence of the following features: F8 (value equal to V0), F25 (value equal to V15), F9 (value equal to V2), F2 and F27 (equal to V0).",
"Compare and contrast the impact of the following features (F7 (equal to V3), F24 (when it is equal to V2) and F3 (value equal to V2)) on the model’s prediction of C1.",
"Describe the degree of impact of the following features: F31, F5 and F32 (value equal to V1)?"
] | [
"F8",
"F25",
"F9",
"F2",
"F27",
"F7",
"F24",
"F3",
"F31",
"F5",
"F32",
"F22",
"F23",
"F18",
"F21",
"F4",
"F1",
"F13",
"F33",
"F19",
"F10",
"F20",
"F26",
"F28",
"F6",
"F14",
"F29",
"F15",
"F17",
"F16",
"F12",
"F11",
"F30"
] | {'F8': 'incident_severity', 'F25': 'insured_hobbies', 'F9': 'insured_relationship', 'F2': 'umbrella_limit', 'F27': 'insured_education_level', 'F7': 'authorities_contacted', 'F24': 'incident_type', 'F3': 'policy_csl', 'F31': 'number_of_vehicles_involved', 'F5': 'capital-loss', 'F32': 'property_damage', 'F22': 'insured_occupation', 'F23': 'age', 'F18': 'incident_state', 'F21': 'insured_zip', 'F4': 'collision_type', 'F1': 'property_claim', 'F13': 'injury_claim', 'F33': 'capital-gains', 'F19': 'witnesses', 'F10': 'incident_city', 'F20': 'police_report_available', 'F26': 'months_as_customer', 'F28': 'auto_year', 'F6': 'insured_sex', 'F14': 'policy_state', 'F29': 'vehicle_claim', 'F15': 'total_claim_amount', 'F17': 'bodily_injuries', 'F16': 'incident_hour_of_the_day', 'F12': 'policy_annual_premium', 'F11': 'policy_deductable', 'F30': 'auto_make'} | {'F27': 'F8', 'F23': 'F25', 'F24': 'F9', 'F5': 'F2', 'F21': 'F27', 'F28': 'F7', 'F25': 'F24', 'F19': 'F3', 'F10': 'F31', 'F8': 'F5', 'F31': 'F32', 'F22': 'F22', 'F2': 'F23', 'F29': 'F18', 'F6': 'F21', 'F26': 'F4', 'F15': 'F1', 'F14': 'F13', 'F7': 'F33', 'F12': 'F19', 'F30': 'F10', 'F32': 'F20', 'F1': 'F26', 'F17': 'F28', 'F20': 'F6', 'F18': 'F14', 'F16': 'F29', 'F13': 'F15', 'F11': 'F17', 'F9': 'F16', 'F4': 'F12', 'F3': 'F11', 'F33': 'F30'} | {'C1': 'C1', 'C2': 'C2'} | Not Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
RandomForestClassifier | C2 | Ethereum Fraud Detection | According to the classification algorithm, the best label for the given case is C2, because there is little to no chance that C1 is the correct label. Not all of the features are found to contribute to the label given here. The following significant features are ordered in order of their effect on the algorithm's output: F36, F8, F26, F35, F3, F12, F24, F9, F21, F6, F20, F5, F4, F25, F19, F27, F7, F23, F37, F31. F30, F33, and F13, on the other hand, are unimportant features since they have almost no influence. Among the most influential features F36, F8, F26, F35, and F3, F26 is considered the most negative, dragging the verdict in a different direction, while the others have positive contributions, increasing the possibility that C2 is correct in this case. F24 is recognised as a positive feature with modest effect, whereas F12 and F9 are identified as negative features. Given that the majority of the top five attributes have positive contributions, boosting the likelihood that C2 is the correct label, it is not unexpected that the algorithm is quite confident in the assigned label's accuracy. | [
"0.08",
"0.04",
"-0.04",
"0.04",
"0.03",
"-0.03",
"0.03",
"-0.03",
"-0.02",
"-0.02",
"-0.02",
"0.02",
"-0.01",
"0.01",
"-0.01",
"-0.01",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 233 | 330 | {'C1': '0.00%', 'C2': '100.00%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F36, F8, F26, F35 and F3.",
"Summarize the direction of influence of the features (F12, F24 and F9) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F36",
"F8",
"F26",
"F35",
"F3",
"F12",
"F24",
"F9",
"F21",
"F6",
"F20",
"F5",
"F4",
"F25",
"F19",
"F27",
"F7",
"F23",
"F37",
"F31",
"F30",
"F33",
"F13",
"F28",
"F11",
"F10",
"F22",
"F2",
"F29",
"F15",
"F1",
"F38",
"F14",
"F32",
"F16",
"F34",
"F17",
"F18"
] | {'F36': ' ERC20 total Ether sent contract', 'F8': ' ERC20 min val rec', 'F26': 'total transactions (including tnx to create contract', 'F35': ' ERC20 max val rec', 'F3': ' Total ERC20 tnxs', 'F12': ' ERC20 uniq rec addr', 'F24': 'min val sent', 'F9': 'Time Diff between first and last (Mins)', 'F21': 'Sent tnx', 'F6': 'Avg min between received tnx', 'F20': 'min value received', 'F5': ' ERC20 total ether sent', 'F4': 'avg val sent', 'F25': 'max val sent', 'F19': 'Avg min between sent tnx', 'F27': 'Received Tnx', 'F7': ' ERC20 uniq sent token name', 'F23': 'Unique Sent To Addresses', 'F37': ' ERC20 uniq rec token name', 'F31': ' ERC20 uniq rec contract addr', 'F30': 'total Ether sent', 'F33': 'Number of Created Contracts', 'F13': ' ERC20 avg val sent', 'F28': ' ERC20 max val sent', 'F11': ' ERC20 min val sent', 'F10': ' ERC20 avg val rec', 'F22': 'Unique Received From Addresses', 'F2': 'max value received ', 'F29': ' ERC20 uniq sent addr.1', 'F15': 'total ether sent contracts', 'F1': 'avg val received', 'F38': ' ERC20 uniq sent addr', 'F14': 'min value sent to contract', 'F32': 'max val sent to contract', 'F16': ' ERC20 total Ether received', 'F34': 'avg value sent to contract', 'F17': 'total ether balance', 'F18': 'total ether received'} | {'F26': 'F36', 'F31': 'F8', 'F18': 'F26', 'F32': 'F35', 'F23': 'F3', 'F28': 'F12', 'F12': 'F24', 'F3': 'F9', 'F4': 'F21', 'F2': 'F6', 'F9': 'F20', 'F25': 'F5', 'F14': 'F4', 'F13': 'F25', 'F1': 'F19', 'F5': 'F27', 'F37': 'F7', 'F8': 'F23', 'F38': 'F37', 'F30': 'F31', 'F19': 'F30', 'F6': 'F33', 'F36': 'F13', 'F35': 'F28', 'F34': 'F11', 'F33': 'F10', 'F7': 'F22', 'F10': 'F2', 'F29': 'F29', 'F21': 'F15', 'F11': 'F1', 'F27': 'F38', 'F15': 'F14', 'F16': 'F32', 'F24': 'F16', 'F17': 'F34', 'F22': 'F17', 'F20': 'F18'} | {'C1': 'C1', 'C2': 'C2'} | Fraud | {'C1': 'Not Fraud', 'C2': 'Fraud'} |
BernoulliNB | C2 | Hotel Satisfaction | The classifier labbelled the given case as C2 with a confidence level of 98.89%, implying that the chance of C1 being the correct label is only about 1.11%. The classification output decision is solely based on the information supplied to the classifier about the case under review. We can rank the contributions of the features as follows: F12, F6, F8, F3, F13, F15, F5, F1, F10, F2, F9, F14, F7, F11, and F4. Among the top features, F12 is the only negative feature, increasing the probability of predicting the alternative label, C1. Other top features that are shifting the prediction towards C2 are F6, F8, and F3. Similar to F12, the features F5, F11, and F2 have negative contributions, supporting the generation of C1. By comparing the strong joint positive attribution to the joint negative attribution, it is evident why the classifier is very certain that C2 is the right label for this instance. | [
"-0.47",
"0.45",
"0.15",
"0.11",
"0.09",
"0.07",
"-0.06",
"0.05",
"0.04",
"-0.04",
"0.04",
"0.03",
"0.03",
"-0.02",
"0.01"
] | [
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negative",
"positive"
] | 16 | 372 | {'C2': '98.89%', 'C1': '1.11%'} | [
"Summarize the prediction for the given test example?",
"In two sentences, provide a brief overview of the features with a higher impact on the model's output prediction.",
"Compare and contrast the impact of the following attributes (F3, F13 and F15) on the model’s prediction of C2.",
"Summarize the set of features has little to no impact on the prediction?"
] | [
"F12",
"F6",
"F8",
"F3",
"F13",
"F15",
"F5",
"F1",
"F10",
"F2",
"F9",
"F14",
"F7",
"F11",
"F4"
] | {'F12': 'Type of Travel', 'F6': 'Type Of Booking', 'F8': 'Common Room entertainment', 'F3': 'Stay comfort', 'F13': 'Cleanliness', 'F15': 'Hotel wifi service', 'F5': 'Other service', 'F1': 'Ease of Online booking', 'F10': 'Age', 'F2': 'Checkin\\/Checkout service', 'F9': 'Food and drink', 'F14': 'Departure\\/Arrival convenience', 'F7': 'purpose_of_travel', 'F11': 'Hotel location', 'F4': 'Gender'} | {'F3': 'F12', 'F4': 'F6', 'F12': 'F8', 'F11': 'F3', 'F15': 'F13', 'F6': 'F15', 'F14': 'F5', 'F8': 'F1', 'F5': 'F10', 'F13': 'F2', 'F10': 'F9', 'F7': 'F14', 'F2': 'F7', 'F9': 'F11', 'F1': 'F4'} | {'C1': 'C2', 'C2': 'C1'} | dissatisfied | {'C2': 'dissatisfied', 'C1': 'satisfied'} |
RandomForestClassifier | C2 | Used Cars Price-Range Prediction | The prediction probability associated with class C1 is 10.50%, while that of class C2 is 89.50%, therefore, it can be concluded that C2 is the most probable label for the given case according to the model. All the input features are shown to contribute to the above decision, and the ones with the strongest influence on the classification decision are F8, F7, and F1, but F10, F4, F6, and F3 are shown to be the least relevant features . Finally, the degree of influence of F9, F5, and F2 can be described as moderate. The model's high confidence can be attributed to the strong positive contributions of F7 and F8 which are supported by the contributions of the remaining positive features F9, F10, and F4. Conversely, shifting the prediction in favour of C1, the negative features F1, F5, F6, F2, and F3. | [
"0.24",
"0.23",
"-0.14",
"0.12",
"-0.10",
"-0.03",
"0.01",
"-0.01",
"0.01",
"-0.00"
] | [
"positive",
"positive",
"negative",
"positive",
"negative",
"negative",
"positive",
"negative",
"positive",
"negative"
] | 259 | 169 | {'C1': '10.50%', 'C2': '89.50%'} | [
"For this test instance, provide information on the predicted label along with the confidence level of the model's decision.",
"Summarize the top features influencing the model's decision along with the respective directions of influence on the prediction?",
"Summarize the direction of influence of the features (F2, F10 and F6) with moderate impact on the prediction made for this test case."
] | [
"F7",
"F8",
"F1",
"F9",
"F5",
"F2",
"F10",
"F6",
"F4",
"F3"
] | {'F7': 'Power', 'F8': 'car_age', 'F1': 'Transmission', 'F9': 'Fuel_Type', 'F5': 'Name', 'F2': 'Mileage', 'F10': 'Engine', 'F6': 'Owner_Type', 'F4': 'Kilometers_Driven', 'F3': 'Seats'} | {'F4': 'F7', 'F5': 'F8', 'F8': 'F1', 'F7': 'F9', 'F6': 'F5', 'F2': 'F2', 'F3': 'F10', 'F9': 'F6', 'F1': 'F4', 'F10': 'F3'} | {'C1': 'C1', 'C2': 'C2'} | High | {'C1': 'Low', 'C2': 'High'} |
SVC | C2 | Food Ordering Customer Churn Prediction | The model labels the case as C2 with fairly high confidence equal to 89.73%, whereas the likelihood of C1 is only 10.27%. Analysis shows that only 20 of the 46 input variables contribute to the prediction assertion above. The prediction judgement C2 is mainly based on the variables F17, F9, F18, and F19. F43, F23, F32, F33, F29, and F20 also contribute to the decision, however, their degree of influence is only moderate. According to the direction of influence analysis, F17, F19, F29, and F33 positively support the decision of the model to assign the label C2. However, F9, F23, F20, F18, F43, and F32 reduce the likelihood or chance that C2 is the true label for this particular test instance. The main variables with less influence on the above classification decision are F14, F31, F7, and F38. | [
"0.12",
"-0.11",
"0.07",
"-0.06",
"-0.05",
"-0.05",
"-0.05",
"-0.05",
"0.05",
"0.05",
"0.05",
"0.04",
"0.04",
"-0.04",
"0.04",
"0.03",
"-0.03",
"0.03",
"0.03",
"0.03",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00",
"0.00"
] | [
"positive",
"negative",
"positive",
"negative",
"negative",
"negative",
"negative",
"negative",
"positive",
"positive",
"positive",
"positive",
"positive",
"negative",
"positive",
"positive",
"negative",
"positive",
"positive",
"positive",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible",
"negligible"
] | 173 | 219 | {'C2': '89.73%', 'C1': '10.27%'} | [
"Summarize the prediction made for the test under consideration along with the likelihood of the different possible class labels.",
"Provide a statement summarizing the ranking of the features as shown in the feature impact plot.",
"Compare the direction of impact of the features: F17 and F9.",
"Summarize the direction of influence of the features (F19, F18, F43 and F23) with moderate impact on the prediction made for this test case.",
"Provide a statement on the features with the least impact on the prediction made for this test case."
] | [
"F17",
"F9",
"F19",
"F18",
"F43",
"F23",
"F32",
"F20",
"F33",
"F29",
"F24",
"F46",
"F42",
"F5",
"F26",
"F13",
"F15",
"F34",
"F30",
"F1",
"F14",
"F7",
"F31",
"F38",
"F12",
"F11",
"F37",
"F28",
"F10",
"F27",
"F21",
"F40",
"F6",
"F25",
"F2",
"F4",
"F44",
"F36",
"F39",
"F41",
"F16",
"F8",
"F3",
"F22",
"F45",
"F35"
] | {'F17': 'Ease and convenient', 'F9': 'Unaffordable', 'F19': 'Good Food quality', 'F18': 'Wrong order delivered', 'F43': 'Delay of delivery person picking up food', 'F23': 'Politeness', 'F32': 'Self Cooking', 'F20': 'Late Delivery', 'F33': 'Health Concern', 'F29': 'More Offers and Discount', 'F24': 'Easy Payment option', 'F46': 'Time saving', 'F42': 'Perference(P2)', 'F5': 'Gender', 'F26': 'Good Road Condition', 'F13': 'Google Maps Accuracy', 'F15': 'Good Taste ', 'F34': 'Good Tracking system', 'F30': 'Bad past experience', 'F1': 'Marital Status', 'F14': 'Influence of rating', 'F7': 'Delivery person ability', 'F31': 'Low quantity low time', 'F38': 'Age', 'F12': 'Less Delivery time', 'F11': 'High Quality of package', 'F37': 'Maximum wait time', 'F28': 'Number of calls', 'F10': 'Freshness ', 'F27': 'Temperature', 'F21': 'Residence in busy location', 'F40': 'Long delivery time', 'F6': 'Order Time', 'F25': 'Influence of time', 'F2': 'Order placed by mistake', 'F4': 'Missing item', 'F44': 'Delay of delivery person getting assigned', 'F36': 'Family size', 'F39': 'Unavailability', 'F41': 'Poor Hygiene', 'F16': 'More restaurant choices', 'F8': 'Perference(P1)', 'F3': 'Educational Qualifications', 'F22': 'Monthly Income', 'F45': 'Occupation', 'F35': 'Good Quantity'} | {'F10': 'F17', 'F23': 'F9', 'F15': 'F19', 'F27': 'F18', 'F26': 'F43', 'F42': 'F23', 'F17': 'F32', 'F19': 'F20', 'F18': 'F33', 'F14': 'F29', 'F13': 'F24', 'F11': 'F46', 'F9': 'F42', 'F2': 'F5', 'F35': 'F26', 'F34': 'F13', 'F45': 'F15', 'F16': 'F34', 'F21': 'F30', 'F3': 'F1', 'F38': 'F14', 'F37': 'F7', 'F36': 'F31', 'F1': 'F38', 'F39': 'F12', 'F40': 'F11', 'F32': 'F37', 'F41': 'F28', 'F43': 'F10', 'F44': 'F27', 'F33': 'F21', 'F24': 'F40', 'F31': 'F6', 'F30': 'F25', 'F29': 'F2', 'F28': 'F4', 'F25': 'F44', 'F7': 'F36', 'F22': 'F39', 'F20': 'F41', 'F12': 'F16', 'F8': 'F8', 'F6': 'F3', 'F5': 'F22', 'F4': 'F45', 'F46': 'F35'} | {'C1': 'C2', 'C2': 'C1'} | Return | {'C2': 'Return', 'C1': 'Go Away'} |
End of preview. Expand
in Dataset Viewer.
- Downloads last month
- 0