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pushing files to the repo from the example!

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  1. README.md +225 -0
  2. config.json +195 -0
  3. confusion_matrix.png +0 -0
  4. example.pkl +3 -0
README.md ADDED
@@ -0,0 +1,225 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license: mit
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+ library_name: sklearn
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+ tags:
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+ - sklearn
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+ - skops
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+ - tabular-classification
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+ model_file: example.pkl
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+ widget:
10
+ structuredData:
11
+ area error:
12
+ - 30.29
13
+ - 96.05
14
+ - 48.31
15
+ compactness error:
16
+ - 0.01911
17
+ - 0.01652
18
+ - 0.01484
19
+ concave points error:
20
+ - 0.01037
21
+ - 0.0137
22
+ - 0.01093
23
+ concavity error:
24
+ - 0.02701
25
+ - 0.02269
26
+ - 0.02813
27
+ fractal dimension error:
28
+ - 0.003586
29
+ - 0.001698
30
+ - 0.002461
31
+ mean area:
32
+ - 481.9
33
+ - 1130.0
34
+ - 748.9
35
+ mean compactness:
36
+ - 0.1058
37
+ - 0.1029
38
+ - 0.1223
39
+ mean concave points:
40
+ - 0.03821
41
+ - 0.07951
42
+ - 0.08087
43
+ mean concavity:
44
+ - 0.08005
45
+ - 0.108
46
+ - 0.1466
47
+ mean fractal dimension:
48
+ - 0.06373
49
+ - 0.05461
50
+ - 0.05796
51
+ mean perimeter:
52
+ - 81.09
53
+ - 123.6
54
+ - 101.7
55
+ mean radius:
56
+ - 12.47
57
+ - 18.94
58
+ - 15.46
59
+ mean smoothness:
60
+ - 0.09965
61
+ - 0.09009
62
+ - 0.1092
63
+ mean symmetry:
64
+ - 0.1925
65
+ - 0.1582
66
+ - 0.1931
67
+ mean texture:
68
+ - 18.6
69
+ - 21.31
70
+ - 19.48
71
+ perimeter error:
72
+ - 2.497
73
+ - 5.486
74
+ - 3.094
75
+ radius error:
76
+ - 0.3961
77
+ - 0.7888
78
+ - 0.4743
79
+ smoothness error:
80
+ - 0.006953
81
+ - 0.004444
82
+ - 0.00624
83
+ symmetry error:
84
+ - 0.01782
85
+ - 0.01386
86
+ - 0.01397
87
+ texture error:
88
+ - 1.044
89
+ - 0.7975
90
+ - 0.7859
91
+ worst area:
92
+ - 677.9
93
+ - 1866.0
94
+ - 1156.0
95
+ worst compactness:
96
+ - 0.2378
97
+ - 0.2336
98
+ - 0.2394
99
+ worst concave points:
100
+ - 0.1015
101
+ - 0.1789
102
+ - 0.1514
103
+ worst concavity:
104
+ - 0.2671
105
+ - 0.2687
106
+ - 0.3791
107
+ worst fractal dimension:
108
+ - 0.0875
109
+ - 0.06589
110
+ - 0.08019
111
+ worst perimeter:
112
+ - 96.05
113
+ - 165.9
114
+ - 124.9
115
+ worst radius:
116
+ - 14.97
117
+ - 24.86
118
+ - 19.26
119
+ worst smoothness:
120
+ - 0.1426
121
+ - 0.1193
122
+ - 0.1546
123
+ worst symmetry:
124
+ - 0.3014
125
+ - 0.2551
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+ - 0.2837
127
+ worst texture:
128
+ - 24.64
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+ - 26.58
130
+ - 26.0
131
+ ---
132
+
133
+ # Model description
134
+
135
+ This is a DecisionTreeClassifier model trained on breast cancer dataset.
136
+
137
+ ## Intended uses & limitations
138
+
139
+ This model is not ready to be used in production.
140
+
141
+ ## Training Procedure
142
+
143
+ ### Hyperparameters
144
+
145
+ The model is trained with below hyperparameters.
146
+
147
+ <details>
148
+ <summary> Click to expand </summary>
149
+
150
+ | Hyperparameter | Value |
151
+ |--------------------------|---------|
152
+ | ccp_alpha | 0.0 |
153
+ | class_weight | |
154
+ | criterion | gini |
155
+ | max_depth | |
156
+ | max_features | |
157
+ | max_leaf_nodes | |
158
+ | min_impurity_decrease | 0.0 |
159
+ | min_samples_leaf | 1 |
160
+ | min_samples_split | 2 |
161
+ | min_weight_fraction_leaf | 0.0 |
162
+ | random_state | |
163
+ | splitter | best |
164
+
165
+ </details>
166
+
167
+ ### Model Plot
168
+
169
+ The model plot is below.
170
+
171
+ <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>DecisionTreeClassifier()</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"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>
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+
173
+ ## Evaluation Results
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+
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+ You can find the details about evaluation process and the evaluation results.
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+
177
+
178
+
179
+ | Metric | Value |
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+ |----------|----------|
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+ | accuracy | 0.929825 |
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+ | f1 score | 0.929825 |
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+
184
+ # How to Get Started with the Model
185
+
186
+ Use the code below to get started with the model.
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+
188
+ ```python
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+ import joblib
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+ import json
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+ import pandas as pd
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+ clf = joblib.load(example.pkl)
193
+ with open("config.json") as f:
194
+ config = json.load(f)
195
+ clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
196
+ ```
197
+
198
+
199
+ # Model Card Authors
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+
201
+ This model card is written by following authors:
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+
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+ skops_user
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+
205
+ # Model Card Contact
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+
207
+ You can contact the model card authors through following channels:
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+ [More Information Needed]
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+
210
+ # Citation
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+
212
+ Below you can find information related to citation.
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+
214
+ **BibTeX:**
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+ ```
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+ bibtex
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+ @inproceedings{...,year={2020}}
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+ ```
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+
220
+
221
+ # Additional Content
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+
223
+ ## confusion_matrix
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+
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+ ![confusion_matrix](confusion_matrix.png)
config.json ADDED
@@ -0,0 +1,195 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "sklearn": {
3
+ "columns": [
4
+ "mean radius",
5
+ "mean texture",
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+ "mean perimeter",
7
+ "mean area",
8
+ "mean smoothness",
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+ "mean compactness",
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+ "mean concavity",
11
+ "mean concave points",
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+ "mean symmetry",
13
+ "mean fractal dimension",
14
+ "radius error",
15
+ "texture error",
16
+ "perimeter error",
17
+ "area error",
18
+ "smoothness error",
19
+ "compactness error",
20
+ "concavity error",
21
+ "concave points error",
22
+ "symmetry error",
23
+ "fractal dimension error",
24
+ "worst radius",
25
+ "worst texture",
26
+ "worst perimeter",
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+ "worst area",
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+ "worst smoothness",
29
+ "worst compactness",
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+ "worst concavity",
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+ "worst concave points",
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+ "worst symmetry",
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+ "worst fractal dimension"
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+ ],
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+ "environment": [
36
+ "scikit-learn=1.1.2"
37
+ ],
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+ "example_input": {
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+ "area error": [
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+ 30.29,
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+ 96.05,
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+ 48.31
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+ ],
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+ "compactness error": [
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+ 0.01911,
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+ 0.01652,
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+ 0.01484
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+ ],
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+ "concave points error": [
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+ 0.01037,
51
+ 0.0137,
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+ 0.01093
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+ ],
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+ "concavity error": [
55
+ 0.02701,
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+ 0.02269,
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+ 0.02813
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+ ],
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+ "fractal dimension error": [
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+ 0.003586,
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+ 0.001698,
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+ 0.002461
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+ ],
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+ "mean area": [
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+ 481.9,
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+ 1130.0,
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+ 748.9
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+ ],
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+ "mean compactness": [
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+ 0.1058,
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+ 0.1029,
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+ 0.1223
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+ ],
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+ "mean concave points": [
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+ 0.03821,
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+ 0.07951,
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+ 0.08087
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+ ],
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+ "mean concavity": [
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+ 0.08005,
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+ 0.108,
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+ 0.1466
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+ ],
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+ "mean fractal dimension": [
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+ 0.06373,
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+ 0.05461,
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+ 0.05796
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+ ],
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+ "mean perimeter": [
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+ 81.09,
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+ 123.6,
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+ 101.7
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+ ],
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+ "mean radius": [
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+ 12.47,
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+ 18.94,
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+ 15.46
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+ ],
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+ "mean smoothness": [
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+ 0.09965,
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+ 0.09009,
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+ 0.1092
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+ ],
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+ "mean symmetry": [
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+ 0.1925,
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+ 0.1582,
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+ 0.1931
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+ ],
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+ "mean texture": [
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+ 18.6,
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+ 21.31,
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+ 19.48
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+ ],
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+ "perimeter error": [
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+ 2.497,
116
+ 5.486,
117
+ 3.094
118
+ ],
119
+ "radius error": [
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+ 0.3961,
121
+ 0.7888,
122
+ 0.4743
123
+ ],
124
+ "smoothness error": [
125
+ 0.006953,
126
+ 0.004444,
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+ 0.00624
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+ ],
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+ "symmetry error": [
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+ 0.01782,
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+ 0.01386,
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+ 0.01397
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+ ],
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+ "texture error": [
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+ 1.044,
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+ 0.7975,
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+ 0.7859
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+ ],
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+ "worst area": [
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+ 677.9,
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+ 1866.0,
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+ 1156.0
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+ ],
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+ "worst compactness": [
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+ 0.2378,
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+ 0.2336,
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+ 0.2394
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+ ],
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+ "worst concave points": [
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+ 0.1015,
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+ 0.1789,
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+ 0.1514
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+ ],
154
+ "worst concavity": [
155
+ 0.2671,
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+ 0.2687,
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+ 0.3791
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+ ],
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+ "worst fractal dimension": [
160
+ 0.0875,
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+ 0.06589,
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+ 0.08019
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+ ],
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+ "worst perimeter": [
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+ 96.05,
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+ 165.9,
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+ 124.9
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+ ],
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+ "worst radius": [
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+ 14.97,
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+ 24.86,
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+ 19.26
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+ ],
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+ "worst smoothness": [
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+ 0.1426,
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+ 0.1193,
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+ 0.1546
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+ ],
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+ "worst symmetry": [
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+ 0.3014,
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+ 0.2551,
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+ 0.2837
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+ ],
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+ "worst texture": [
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+ 24.64,
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+ 26.58,
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+ 26.0
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+ ]
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+ },
190
+ "model": {
191
+ "file": "example.pkl"
192
+ },
193
+ "task": "tabular-classification"
194
+ }
195
+ }
confusion_matrix.png ADDED
example.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:64108688e241a1d7c1e94a4d3539f03fe844257119eb2af7c8a6d8de112ba5a6
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+ size 3924