pushing files to the repo from the example!
Browse files- README.md +249 -0
- config.json +129 -0
- confusion_matrix.png +0 -0
- example.pkl +3 -0
README.md
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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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widget:
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structuredData:
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Contract:
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- Two year
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- Month-to-month
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- One year
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+
Dependents:
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- 'Yes'
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- 'No'
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- 'No'
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+
DeviceProtection:
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- 'No'
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- 'No'
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- 'Yes'
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+
InternetService:
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- Fiber optic
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- Fiber optic
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- DSL
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+
MonthlyCharges:
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- 79.05
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- 84.95
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- 68.8
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+
MultipleLines:
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- 'Yes'
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- 'Yes'
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- 'Yes'
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+
OnlineBackup:
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- 'No'
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- 'No'
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- 'Yes'
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OnlineSecurity:
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- 'Yes'
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- 'No'
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- 'Yes'
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PaperlessBilling:
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- 'No'
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- 'Yes'
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- 'No'
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Partner:
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- 'Yes'
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- 'Yes'
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- 'No'
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PaymentMethod:
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- Bank transfer (automatic)
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- Electronic check
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- Bank transfer (automatic)
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PhoneService:
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- 'Yes'
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- 'Yes'
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- 'Yes'
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+
SeniorCitizen:
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- 0
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- 0
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- 0
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+
StreamingMovies:
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- 'No'
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- 'No'
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- 'No'
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StreamingTV:
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- 'No'
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- 'Yes'
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- 'No'
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TechSupport:
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- 'No'
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- 'No'
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- 'Yes'
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+
TotalCharges:
|
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- 5730.7
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- 1378.25
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- 4111.35
|
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+
gender:
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- Female
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- Female
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- Male
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+
tenure:
|
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- 72
|
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+
- 16
|
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- 63
|
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+
---
|
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+
|
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# Model description
|
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|
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This is a Logistic Regression model trained on churn dataset.
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|
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## Intended uses & limitations
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|
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This model is not ready to be used in production.
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|
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## Training Procedure
|
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+
|
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### Hyperparameters
|
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+
|
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The model is trained with below hyperparameters.
|
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+
|
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<details>
|
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<summary> Click to expand </summary>
|
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+
|
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| Hyperparameter | Value |
|
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+
|--------------------------------------------|-----------------------------------------------------------------------------------|
|
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| memory | |
|
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| steps | [('preprocessor', ColumnTransformer(transformers=[('num',
|
109 |
+
Pipeline(steps=[('imputer',
|
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+
SimpleImputer(strategy='median')),
|
111 |
+
('std_scaler',
|
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+
StandardScaler())]),
|
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+
['MonthlyCharges', 'TotalCharges', 'tenure']),
|
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+
('cat', OneHotEncoder(),
|
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+
['SeniorCitizen', 'gender', 'Partner',
|
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+
'Dependents', 'PhoneService', 'MultipleLines',
|
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'InternetService', 'OnlineSecurity',
|
118 |
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'OnlineBackup', 'DeviceProtection',
|
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+
'TechSupport', 'StreamingTV',
|
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'StreamingMovies', 'Contract',
|
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+
'PaperlessBilling', 'PaymentMethod'])])), ('classifier', LogisticRegression(class_weight='balanced', max_iter=300))] |
|
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+
| verbose | False |
|
123 |
+
| preprocessor | ColumnTransformer(transformers=[('num',
|
124 |
+
Pipeline(steps=[('imputer',
|
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+
SimpleImputer(strategy='median')),
|
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+
('std_scaler',
|
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+
StandardScaler())]),
|
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['MonthlyCharges', 'TotalCharges', 'tenure']),
|
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+
('cat', OneHotEncoder(),
|
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['SeniorCitizen', 'gender', 'Partner',
|
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'Dependents', 'PhoneService', 'MultipleLines',
|
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'InternetService', 'OnlineSecurity',
|
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'OnlineBackup', 'DeviceProtection',
|
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'TechSupport', 'StreamingTV',
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'StreamingMovies', 'Contract',
|
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'PaperlessBilling', 'PaymentMethod'])]) |
|
137 |
+
| classifier | LogisticRegression(class_weight='balanced', max_iter=300) |
|
138 |
+
| preprocessor__n_jobs | |
|
139 |
+
| preprocessor__remainder | drop |
|
140 |
+
| preprocessor__sparse_threshold | 0.3 |
|
141 |
+
| preprocessor__transformer_weights | |
|
142 |
+
| preprocessor__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
|
143 |
+
('std_scaler', StandardScaler())]), ['MonthlyCharges', 'TotalCharges', 'tenure']), ('cat', OneHotEncoder(), ['SeniorCitizen', 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod'])] |
|
144 |
+
| preprocessor__verbose | False |
|
145 |
+
| preprocessor__verbose_feature_names_out | True |
|
146 |
+
| preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),
|
147 |
+
('std_scaler', StandardScaler())]) |
|
148 |
+
| preprocessor__cat | OneHotEncoder() |
|
149 |
+
| preprocessor__num__memory | |
|
150 |
+
| preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())] |
|
151 |
+
| preprocessor__num__verbose | False |
|
152 |
+
| preprocessor__num__imputer | SimpleImputer(strategy='median') |
|
153 |
+
| preprocessor__num__std_scaler | StandardScaler() |
|
154 |
+
| preprocessor__num__imputer__add_indicator | False |
|
155 |
+
| preprocessor__num__imputer__copy | True |
|
156 |
+
| preprocessor__num__imputer__fill_value | |
|
157 |
+
| preprocessor__num__imputer__missing_values | nan |
|
158 |
+
| preprocessor__num__imputer__strategy | median |
|
159 |
+
| preprocessor__num__imputer__verbose | deprecated |
|
160 |
+
| preprocessor__num__std_scaler__copy | True |
|
161 |
+
| preprocessor__num__std_scaler__with_mean | True |
|
162 |
+
| preprocessor__num__std_scaler__with_std | True |
|
163 |
+
| preprocessor__cat__categories | auto |
|
164 |
+
| preprocessor__cat__drop | |
|
165 |
+
| preprocessor__cat__dtype | <class 'numpy.float64'> |
|
166 |
+
| preprocessor__cat__handle_unknown | error |
|
167 |
+
| preprocessor__cat__max_categories | |
|
168 |
+
| preprocessor__cat__min_frequency | |
|
169 |
+
| preprocessor__cat__sparse | True |
|
170 |
+
| classifier__C | 1.0 |
|
171 |
+
| classifier__class_weight | balanced |
|
172 |
+
| classifier__dual | False |
|
173 |
+
| classifier__fit_intercept | True |
|
174 |
+
| classifier__intercept_scaling | 1 |
|
175 |
+
| classifier__l1_ratio | |
|
176 |
+
| classifier__max_iter | 300 |
|
177 |
+
| classifier__multi_class | auto |
|
178 |
+
| classifier__n_jobs | |
|
179 |
+
| classifier__penalty | l2 |
|
180 |
+
| classifier__random_state | |
|
181 |
+
| classifier__solver | lbfgs |
|
182 |
+
| classifier__tol | 0.0001 |
|
183 |
+
| classifier__verbose | 0 |
|
184 |
+
| classifier__warm_start | False |
|
185 |
+
|
186 |
+
</details>
|
187 |
+
|
188 |
+
### Model Plot
|
189 |
+
|
190 |
+
The model plot is below.
|
191 |
+
|
192 |
+
<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-3" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('std_scaler',StandardScaler())]),['MonthlyCharges','TotalCharges', 'tenure']),('cat', OneHotEncoder(),['SeniorCitizen', 'gender','Partner', 'Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport', 'StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod'])])),('classifier',LogisticRegression(class_weight='balanced', max_iter=300))])</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-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('std_scaler',StandardScaler())]),['MonthlyCharges','TotalCharges', 'tenure']),('cat', OneHotEncoder(),['SeniorCitizen', 'gender','Partner', 'Dependents','PhoneService','MultipleLines','InternetService','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport', 'StreamingTV','StreamingMovies','Contract','PaperlessBilling','PaymentMethod'])])),('classifier',LogisticRegression(class_weight='balanced', max_iter=300))])</pre></div></div></div><div class="sk-serial"><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-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('std_scaler',StandardScaler())]),['MonthlyCharges', 'TotalCharges', 'tenure']),('cat', OneHotEncoder(),['SeniorCitizen', 'gender', 'Partner','Dependents', 'PhoneService', 'MultipleLines','InternetService', 'OnlineSecurity','OnlineBackup', 'DeviceProtection','TechSupport', 'StreamingTV','StreamingMovies', 'Contract','PaperlessBilling', 'PaymentMethod'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-12" type="checkbox" ><label for="sk-estimator-id-12" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>['MonthlyCharges', 'TotalCharges', 'tenure']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><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-13" type="checkbox" ><label for="sk-estimator-id-13" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</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-14" type="checkbox" ><label for="sk-estimator-id-14" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-15" type="checkbox" ><label for="sk-estimator-id-15" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['SeniorCitizen', 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']</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-16" type="checkbox" ><label for="sk-estimator-id-16" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder()</pre></div></div></div></div></div></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-17" type="checkbox" ><label for="sk-estimator-id-17" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(class_weight='balanced', max_iter=300)</pre></div></div></div></div></div></div></div>
|
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+
|
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+
## 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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|
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+
|
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+
|
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| Metric | Value |
|
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+
|----------|----------|
|
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+
| accuracy | 0.730305 |
|
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+
| f1 score | 0.730305 |
|
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+
|
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+
# How to Get Started with the Model
|
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+
|
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+
Use the code below to get started with the model.
|
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+
|
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+
<details>
|
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+
<summary> Click to expand </summary>
|
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+
|
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+
```python
|
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+
import pickle
|
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+
with open(dtc_pkl_filename, 'rb') as file:
|
215 |
+
clf = pickle.load(file)
|
216 |
+
```
|
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+
|
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+
</details>
|
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+
|
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+
|
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+
|
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+
|
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# Model Card Authors
|
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+
|
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+
This model card is written by following authors:
|
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+
|
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+
skops_user
|
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+
|
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+
# Model Card Contact
|
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+
|
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+
You can contact the model card authors through following channels:
|
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+
[More Information Needed]
|
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+
|
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+
# Citation
|
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+
|
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+
Below you can find information related to citation.
|
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+
|
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+
**BibTeX:**
|
239 |
+
```
|
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+
bibtex
|
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+
@inproceedings{...,year={2020}}
|
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+
```
|
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+
|
244 |
+
|
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+
# Additional Content
|
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+
|
247 |
+
## confusion_matrix
|
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+
|
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+
![confusion_matrix](confusion_matrix.png)
|
config.json
ADDED
@@ -0,0 +1,129 @@
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1 |
+
{
|
2 |
+
"sklearn": {
|
3 |
+
"columns": [
|
4 |
+
"gender",
|
5 |
+
"SeniorCitizen",
|
6 |
+
"Partner",
|
7 |
+
"Dependents",
|
8 |
+
"tenure",
|
9 |
+
"PhoneService",
|
10 |
+
"MultipleLines",
|
11 |
+
"InternetService",
|
12 |
+
"OnlineSecurity",
|
13 |
+
"OnlineBackup",
|
14 |
+
"DeviceProtection",
|
15 |
+
"TechSupport",
|
16 |
+
"StreamingTV",
|
17 |
+
"StreamingMovies",
|
18 |
+
"Contract",
|
19 |
+
"PaperlessBilling",
|
20 |
+
"PaymentMethod",
|
21 |
+
"MonthlyCharges",
|
22 |
+
"TotalCharges"
|
23 |
+
],
|
24 |
+
"environment": [
|
25 |
+
"scikit-learn=1.1.1"
|
26 |
+
],
|
27 |
+
"example_input": {
|
28 |
+
"Contract": [
|
29 |
+
"Two year",
|
30 |
+
"Month-to-month",
|
31 |
+
"One year"
|
32 |
+
],
|
33 |
+
"Dependents": [
|
34 |
+
"Yes",
|
35 |
+
"No",
|
36 |
+
"No"
|
37 |
+
],
|
38 |
+
"DeviceProtection": [
|
39 |
+
"No",
|
40 |
+
"No",
|
41 |
+
"Yes"
|
42 |
+
],
|
43 |
+
"InternetService": [
|
44 |
+
"Fiber optic",
|
45 |
+
"Fiber optic",
|
46 |
+
"DSL"
|
47 |
+
],
|
48 |
+
"MonthlyCharges": [
|
49 |
+
79.05,
|
50 |
+
84.95,
|
51 |
+
68.8
|
52 |
+
],
|
53 |
+
"MultipleLines": [
|
54 |
+
"Yes",
|
55 |
+
"Yes",
|
56 |
+
"Yes"
|
57 |
+
],
|
58 |
+
"OnlineBackup": [
|
59 |
+
"No",
|
60 |
+
"No",
|
61 |
+
"Yes"
|
62 |
+
],
|
63 |
+
"OnlineSecurity": [
|
64 |
+
"Yes",
|
65 |
+
"No",
|
66 |
+
"Yes"
|
67 |
+
],
|
68 |
+
"PaperlessBilling": [
|
69 |
+
"No",
|
70 |
+
"Yes",
|
71 |
+
"No"
|
72 |
+
],
|
73 |
+
"Partner": [
|
74 |
+
"Yes",
|
75 |
+
"Yes",
|
76 |
+
"No"
|
77 |
+
],
|
78 |
+
"PaymentMethod": [
|
79 |
+
"Bank transfer (automatic)",
|
80 |
+
"Electronic check",
|
81 |
+
"Bank transfer (automatic)"
|
82 |
+
],
|
83 |
+
"PhoneService": [
|
84 |
+
"Yes",
|
85 |
+
"Yes",
|
86 |
+
"Yes"
|
87 |
+
],
|
88 |
+
"SeniorCitizen": [
|
89 |
+
0,
|
90 |
+
0,
|
91 |
+
0
|
92 |
+
],
|
93 |
+
"StreamingMovies": [
|
94 |
+
"No",
|
95 |
+
"No",
|
96 |
+
"No"
|
97 |
+
],
|
98 |
+
"StreamingTV": [
|
99 |
+
"No",
|
100 |
+
"Yes",
|
101 |
+
"No"
|
102 |
+
],
|
103 |
+
"TechSupport": [
|
104 |
+
"No",
|
105 |
+
"No",
|
106 |
+
"Yes"
|
107 |
+
],
|
108 |
+
"TotalCharges": [
|
109 |
+
5730.7,
|
110 |
+
1378.25,
|
111 |
+
4111.35
|
112 |
+
],
|
113 |
+
"gender": [
|
114 |
+
"Female",
|
115 |
+
"Female",
|
116 |
+
"Male"
|
117 |
+
],
|
118 |
+
"tenure": [
|
119 |
+
72,
|
120 |
+
16,
|
121 |
+
63
|
122 |
+
]
|
123 |
+
},
|
124 |
+
"model": {
|
125 |
+
"file": "example.pkl"
|
126 |
+
},
|
127 |
+
"task": "tabular-classification"
|
128 |
+
}
|
129 |
+
}
|
confusion_matrix.png
ADDED
example.pkl
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9b1fd5f00d775eecf3241a589cb379275ed962318659e36a8dcdb6ed304897da
|
3 |
+
size 4438
|