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--- |
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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: churn.pkl |
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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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# Model description |
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This is a Logistic Regression model trained on churn dataset. |
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## Intended uses & limitations |
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This model is not ready to be used in production. |
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## Training Procedure |
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### Hyperparameters |
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The model is trained with below hyperparameters. |
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<details> |
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<summary> Click to expand </summary> |
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| Hyperparameter | Value | |
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|--------------------------------------------|-----------------------------------------------------------------------------------| |
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| memory | | |
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| steps | [('preprocessor', ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('std_scaler',<br /> StandardScaler())]),<br /> ['MonthlyCharges', 'TotalCharges', 'tenure']),<br /> ('cat', OneHotEncoder(handle_unknown='ignore'),<br /> ['SeniorCitizen', 'gender', 'Partner',<br /> 'Dependents', 'PhoneService', 'MultipleLines',<br /> 'InternetService', 'OnlineSecurity',<br /> 'OnlineBackup', 'DeviceProtection',<br /> 'TechSupport', 'StreamingTV',<br /> 'StreamingMovies', 'Contract',<br /> 'PaperlessBilling', 'PaymentMethod'])])), ('classifier', LogisticRegression(class_weight='balanced', max_iter=300))] | |
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| verbose | False | |
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| preprocessor | ColumnTransformer(transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('std_scaler',<br /> StandardScaler())]),<br /> ['MonthlyCharges', 'TotalCharges', 'tenure']),<br /> ('cat', OneHotEncoder(handle_unknown='ignore'),<br /> ['SeniorCitizen', 'gender', 'Partner',<br /> 'Dependents', 'PhoneService', 'MultipleLines',<br /> 'InternetService', 'OnlineSecurity',<br /> 'OnlineBackup', 'DeviceProtection',<br /> 'TechSupport', 'StreamingTV',<br /> 'StreamingMovies', 'Contract',<br /> 'PaperlessBilling', 'PaymentMethod'])]) | |
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| classifier | LogisticRegression(class_weight='balanced', max_iter=300) | |
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| preprocessor__n_jobs | | |
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| preprocessor__remainder | drop | |
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| preprocessor__sparse_threshold | 0.3 | |
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| preprocessor__transformer_weights | | |
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| preprocessor__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('std_scaler', StandardScaler())]), ['MonthlyCharges', 'TotalCharges', 'tenure']), ('cat', OneHotEncoder(handle_unknown='ignore'), ['SeniorCitizen', 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod'])] | |
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| preprocessor__verbose | False | |
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| preprocessor__verbose_feature_names_out | True | |
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| preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('std_scaler', StandardScaler())]) | |
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| preprocessor__cat | OneHotEncoder(handle_unknown='ignore') | |
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| preprocessor__num__memory | | |
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| preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())] | |
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| preprocessor__num__verbose | False | |
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| preprocessor__num__imputer | SimpleImputer(strategy='median') | |
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| preprocessor__num__std_scaler | StandardScaler() | |
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| preprocessor__num__imputer__add_indicator | False | |
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| preprocessor__num__imputer__copy | True | |
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| preprocessor__num__imputer__fill_value | | |
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| preprocessor__num__imputer__missing_values | nan | |
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| preprocessor__num__imputer__strategy | median | |
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| preprocessor__num__imputer__verbose | 0 | |
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| preprocessor__num__std_scaler__copy | True | |
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| preprocessor__num__std_scaler__with_mean | True | |
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| preprocessor__num__std_scaler__with_std | True | |
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| preprocessor__cat__categories | auto | |
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| preprocessor__cat__drop | | |
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| preprocessor__cat__dtype | <class 'numpy.float64'> | |
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| preprocessor__cat__handle_unknown | ignore | |
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| preprocessor__cat__sparse | True | |
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| classifier__C | 1.0 | |
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| classifier__class_weight | balanced | |
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| classifier__dual | False | |
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| classifier__fit_intercept | True | |
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| classifier__intercept_scaling | 1 | |
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| classifier__l1_ratio | | |
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| classifier__max_iter | 300 | |
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| classifier__multi_class | auto | |
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| classifier__n_jobs | | |
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| classifier__penalty | l2 | |
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| classifier__random_state | | |
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| classifier__solver | lbfgs | |
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| classifier__tol | 0.0001 | |
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| classifier__verbose | 0 | |
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| classifier__warm_start | False | |
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</details> |
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### Model Plot |
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The model plot is below. |
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<style>#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 {color: black;background-color: white;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 pre{padding: 0;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-toggleable {background-color: white;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 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-f0122ce0-64cb-41b3-8d66-0b116516efc3 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-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-item {z-index: 1;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-parallel-item:only-child::after {width: 0;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 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;position: relative;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-f0122ce0-64cb-41b3-8d66-0b116516efc3 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-f0122ce0-64cb-41b3-8d66-0b116516efc3 div.sk-text-repr-fallback {display: none;}</style><div id="sk-f0122ce0-64cb-41b3-8d66-0b116516efc3" class="sk-top-container" style="overflow: auto;"><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(handle_unknown='ignore'),['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>Please rerun this cell to show the HTML repr or trust the notebook.</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="193bb424-11e4-4240-a49c-2b9ff9c16021" type="checkbox" ><label for="193bb424-11e4-4240-a49c-2b9ff9c16021" 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(handle_unknown='ignore'),['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="54004708-11cd-4f85-bff3-744af144ae72" type="checkbox" ><label for="54004708-11cd-4f85-bff3-744af144ae72" 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(handle_unknown='ignore'),['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="53cbe948-0bd7-4512-874e-7c0e8287ebf2" type="checkbox" ><label for="53cbe948-0bd7-4512-874e-7c0e8287ebf2" 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="9748155a-6575-4ba1-b5a2-9171c6ac1a11" type="checkbox" ><label for="9748155a-6575-4ba1-b5a2-9171c6ac1a11" 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="27303a89-9235-4743-862c-fa1959656bb7" type="checkbox" ><label for="27303a89-9235-4743-862c-fa1959656bb7" 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="0a07f5b9-db03-4bf5-bc2c-9b3f60e6ab16" type="checkbox" ><label for="0a07f5b9-db03-4bf5-bc2c-9b3f60e6ab16" 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="d985852a-65b0-4b77-897a-82c0ef3fa365" type="checkbox" ><label for="d985852a-65b0-4b77-897a-82c0ef3fa365" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</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="050e23d8-6e98-4cfa-9ff8-cc01091c6a1f" type="checkbox" ><label for="050e23d8-6e98-4cfa-9ff8-cc01091c6a1f" 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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## Evaluation Results |
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You can find the details about evaluation process and the evaluation results. |
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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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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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```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(churn.pkl) |
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with open("config.json") as f: |
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config = json.load(f) |
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clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"])) |
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``` |
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# Model Card Authors |
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This model card is written by following authors: |
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skops_user |
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# Model Card Contact |
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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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# Citation |
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Below you can find information related to citation. |
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**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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# Additional Content |
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## confusion_matrix |
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