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README.md +236 -0
config.json +208 -0
model.pkl +3 -0

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+---
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-regression
+model_format: pickle
+model_file: model.pkl
+widget:
+  structuredData:
+    Fedu:
+    - 3
+    - 3
+    - 3
+    Fjob:
+    - other
+    - other
+    - services
+    G1:
+    - 12
+    - 13
+    - 8
+    G2:
+    - 13
+    - 14
+    - 7
+    G3:
+    - 12
+    - 14
+    - 0
+    Medu:
+    - 3
+    - 2
+    - 1
+    Mjob:
+    - services
+    - other
+    - at_home
+    Pstatus:
+    - T
+    - T
+    - T
+    Walc:
+    - 2
+    - 1
+    - 1
+    absences:
+    - 2
+    - 0
+    - 0
+    activities:
+    - 'yes'
+    - 'no'
+    - 'yes'
+    address:
+    - U
+    - U
+    - U
+    age:
+    - 16
+    - 16
+    - 16
+    failures:
+    - 0
+    - 0
+    - 3
+    famrel:
+    - 4
+    - 5
+    - 4
+    famsize:
+    - GT3
+    - GT3
+    - GT3
+    famsup:
+    - 'no'
+    - 'no'
+    - 'no'
+    freetime:
+    - 2
+    - 3
+    - 3
+    goout:
+    - 3
+    - 3
+    - 5
+    guardian:
+    - mother
+    - father
+    - mother
+    health:
+    - 3
+    - 3
+    - 3
+    higher:
+    - 'yes'
+    - 'yes'
+    - 'yes'
+    internet:
+    - 'yes'
+    - 'yes'
+    - 'yes'
+    nursery:
+    - 'yes'
+    - 'yes'
+    - 'no'
+    paid:
+    - 'yes'
+    - 'no'
+    - 'no'
+    reason:
+    - home
+    - home
+    - home
+    romantic:
+    - 'yes'
+    - 'no'
+    - 'yes'
+    school:
+    - GP
+    - GP
+    - GP
+    schoolsup:
+    - 'no'
+    - 'no'
+    - 'no'
+    sex:
+    - M
+    - M
+    - F
+    studytime:
+    - 2
+    - 1
+    - 2
+    traveltime:
+    - 1
+    - 2
+    - 1
+---
+# Model description
+[More Information Needed]
+## Intended uses & limitations
+[More Information Needed]
+## Training Procedure
+### Hyperparameters
+The model is trained with below hyperparameters.
+<details>
+<summary> Click to expand </summary>
+| Hyperparameter                        | Value                                                |
+|---------------------------------------|------------------------------------------------------|
+| memory                                |                                                      |
+| steps                                 | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False)), ('xgbregressor', XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,<br />             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,<br />             gamma=0, gpu_id=-1, importance_type=None,<br />             interaction_constraints='', learning_rate=0.300000012,<br />             max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,<br />             monotone_constraints='()', n_estimators=100, n_jobs=8,<br />             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,<br />             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',<br />             validate_parameters=1, verbosity=None))]                                                      |
+| verbose                               | False                                                |
+| onehotencoder                         | OneHotEncoder(handle_unknown='ignore', sparse=False) |
+| xgbregressor                          | XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,<br />             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,<br />             gamma=0, gpu_id=-1, importance_type=None,<br />             interaction_constraints='', learning_rate=0.300000012,<br />             max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,<br />             monotone_constraints='()', n_estimators=100, n_jobs=8,<br />             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,<br />             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',<br />             validate_parameters=1, verbosity=None)                                                      |
+| onehotencoder__categories             | auto                                                 |
+| onehotencoder__drop                   |                                                      |
+| onehotencoder__dtype                  | <class 'numpy.float64'>                              |
+| onehotencoder__handle_unknown         | ignore                                               |
+| onehotencoder__max_categories         |                                                      |
+| onehotencoder__min_frequency          |                                                      |
+| onehotencoder__sparse                 | False                                                |
+| xgbregressor__objective               | reg:squarederror                                     |
+| xgbregressor__base_score              | 0.5                                                  |
+| xgbregressor__booster                 | gbtree                                               |
+| xgbregressor__colsample_bylevel       | 1                                                    |
+| xgbregressor__colsample_bynode        | 1                                                    |
+| xgbregressor__colsample_bytree        | 1                                                    |
+| xgbregressor__enable_categorical      | False                                                |
+| xgbregressor__gamma                   | 0                                                    |
+| xgbregressor__gpu_id                  | -1                                                   |
+| xgbregressor__importance_type         |                                                      |
+| xgbregressor__interaction_constraints |                                                      |
+| xgbregressor__learning_rate           | 0.300000012                                          |
+| xgbregressor__max_delta_step          | 0                                                    |
+| xgbregressor__max_depth               | 5                                                    |
+| xgbregressor__min_child_weight        | 1                                                    |
+| xgbregressor__missing                 | nan                                                  |
+| xgbregressor__monotone_constraints    | ()                                                   |
+| xgbregressor__n_estimators            | 100                                                  |
+| xgbregressor__n_jobs                  | 8                                                    |
+| xgbregressor__num_parallel_tree       | 1                                                    |
+| xgbregressor__predictor               | auto                                                 |
+| xgbregressor__random_state            | 0                                                    |
+| xgbregressor__reg_alpha               | 0                                                    |
+| xgbregressor__reg_lambda              | 1                                                    |
+| xgbregressor__scale_pos_weight        | 1                                                    |
+| xgbregressor__subsample               | 1                                                    |
+| xgbregressor__tree_method             | exact                                                |
+| xgbregressor__validate_parameters     | 1                                                    |
+| xgbregressor__verbosity               |                                                      |
+</details>
+### Model Plot
+The model plot is below.
+<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)),(&#x27;xgbregressor&#x27;,XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,colsample_bylevel=1, colsample_bynode=1,colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;,learning_rate=0.300000012, max_delta_step=0,max_depth=5, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100,n_jobs=8, num_parallel_tree=1, predictor=&#x27;auto&#x27;,random_state=0, reg_alpha=0, reg_lambda=1,scale_pos_weight=1, subsample=1,tree_method=&#x27;exact&#x27;, validate_parameters=1,verbosity=None))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;onehotencoder&#x27;,OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)),(&#x27;xgbregressor&#x27;,XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;,colsample_bylevel=1, colsample_bynode=1,colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;,learning_rate=0.300000012, max_delta_step=0,max_depth=5, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100,n_jobs=8, num_parallel_tree=1, predictor=&#x27;auto&#x27;,random_state=0, reg_alpha=0, reg_lambda=1,scale_pos_weight=1, subsample=1,tree_method=&#x27;exact&#x27;, validate_parameters=1,verbosity=None))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse=False)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">XGBRegressor</label><div class="sk-toggleable__content"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, colsample_bylevel=1,colsample_bynode=1, colsample_bytree=1, enable_categorical=False,gamma=0, gpu_id=-1, importance_type=None,interaction_constraints=&#x27;&#x27;, learning_rate=0.300000012,max_delta_step=0, max_depth=5, min_child_weight=1, missing=nan,monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=8,num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method=&#x27;exact&#x27;,validate_parameters=1, verbosity=None)</pre></div></div></div></div></div></div></div>
+## Evaluation Results
+[More Information Needed]
+# How to Get Started with the Model
+[More Information Needed]
+# Model Card Authors
+This model card is written by following authors:
+[More Information Needed]
+# Model Card Contact
+You can contact the model card authors through following channels:
+[More Information Needed]
+# Citation
+Below you can find information related to citation.
+**BibTeX:**
+```
+[More Information Needed]
+```

config.json ADDED Viewed

	@@ -0,0 +1,208 @@

+{
+    "sklearn": {
+        "columns": [
+            "school",
+            "sex",
+            "age",
+            "address",
+            "famsize",
+            "Pstatus",
+            "Medu",
+            "Fedu",
+            "Mjob",
+            "Fjob",
+            "reason",
+            "guardian",
+            "traveltime",
+            "studytime",
+            "failures",
+            "schoolsup",
+            "famsup",
+            "paid",
+            "activities",
+            "nursery",
+            "higher",
+            "internet",
+            "romantic",
+            "famrel",
+            "freetime",
+            "goout",
+            "Walc",
+            "health",
+            "absences",
+            "G1",
+            "G2",
+            "G3"
+        ],
+        "environment": [
+            "scikit-learn=1.1.2, xgboost=1.5.2"
+        ],
+        "example_input": {
+            "Fedu": [
+                3,
+                3,
+                3
+            ],
+            "Fjob": [
+                "other",
+                "other",
+                "services"
+            ],
+            "G1": [
+                12,
+                13,
+                8
+            ],
+            "G2": [
+                13,
+                14,
+                7
+            ],
+            "G3": [
+                12,
+                14,
+                0
+            ],
+            "Medu": [
+                3,
+                2,
+                1
+            ],
+            "Mjob": [
+                "services",
+                "other",
+                "at_home"
+            ],
+            "Pstatus": [
+                "T",
+                "T",
+                "T"
+            ],
+            "Walc": [
+                2,
+                1,
+                1
+            ],
+            "absences": [
+                2,
+                0,
+                0
+            ],
+            "activities": [
+                "yes",
+                "no",
+                "yes"
+            ],
+            "address": [
+                "U",
+                "U",
+                "U"
+            ],
+            "age": [
+                16,
+                16,
+                16
+            ],
+            "failures": [
+                0,
+                0,
+                3
+            ],
+            "famrel": [
+                4,
+                5,
+                4
+            ],
+            "famsize": [
+                "GT3",
+                "GT3",
+                "GT3"
+            ],
+            "famsup": [
+                "no",
+                "no",
+                "no"
+            ],
+            "freetime": [
+                2,
+                3,
+                3
+            ],
+            "goout": [
+                3,
+                3,
+                5
+            ],
+            "guardian": [
+                "mother",
+                "father",
+                "mother"
+            ],
+            "health": [
+                3,
+                3,
+                3
+            ],
+            "higher": [
+                "yes",
+                "yes",
+                "yes"
+            ],
+            "internet": [
+                "yes",
+                "yes",
+                "yes"
+            ],
+            "nursery": [
+                "yes",
+                "yes",
+                "no"
+            ],
+            "paid": [
+                "yes",
+                "no",
+                "no"
+            ],
+            "reason": [
+                "home",
+                "home",
+                "home"
+            ],
+            "romantic": [
+                "yes",
+                "no",
+                "yes"
+            ],
+            "school": [
+                "GP",
+                "GP",
+                "GP"
+            ],
+            "schoolsup": [
+                "no",
+                "no",
+                "no"
+            ],
+            "sex": [
+                "M",
+                "M",
+                "F"
+            ],
+            "studytime": [
+                2,
+                1,
+                2
+            ],
+            "traveltime": [
+                1,
+                2,
+                1
+            ]
+        },
+        "model": {
+            "file": "model.pkl"
+        },
+        "model_format": "pickle",
+        "task": "tabular-regression"
+    }
+}

model.pkl ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:e3fd2d9124da96af9c5bb19e0ae8a90b8c47016b490de4b460cb357f79119815
+size 219144