--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification widget: structuredData: Contract: - Two year - Month-to-month - One year Dependents: - 'Yes' - 'No' - 'No' DeviceProtection: - 'No' - 'No' - 'Yes' InternetService: - Fiber optic - Fiber optic - DSL MonthlyCharges: - 79.05 - 84.95 - 68.8 MultipleLines: - 'Yes' - 'Yes' - 'Yes' OnlineBackup: - 'No' - 'No' - 'Yes' OnlineSecurity: - 'Yes' - 'No' - 'Yes' PaperlessBilling: - 'No' - 'Yes' - 'No' Partner: - 'Yes' - 'Yes' - 'No' PaymentMethod: - Bank transfer (automatic) - Electronic check - Bank transfer (automatic) PhoneService: - 'Yes' - 'Yes' - 'Yes' SeniorCitizen: - 0 - 0 - 0 StreamingMovies: - 'No' - 'No' - 'No' StreamingTV: - 'No' - 'Yes' - 'No' TechSupport: - 'No' - 'No' - 'Yes' TotalCharges: - 5730.7 - 1378.25 - 4111.35 gender: - Female - Female - Male tenure: - 72 - 16 - 63 --- # Model description This is a Logistic Regression model trained on churn dataset. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure ### Hyperparameters The model is trained with below hyperparameters.

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| Hyperparameter | Value | |--------------------------------------------|-----------------------------------------------------------------------------------| | memory | | | 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))] | | verbose | False | | 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) | | preprocessor__n_jobs | | | preprocessor__remainder | drop | | preprocessor__sparse_threshold | 0.3 | | preprocessor__transformer_weights | | | preprocessor__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'])] | | preprocessor__verbose | False | | preprocessor__verbose_feature_names_out | True | | preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())]) | | preprocessor__cat | OneHotEncoder() | | preprocessor__num__memory | | | preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())] | | preprocessor__num__verbose | False | | preprocessor__num__imputer | SimpleImputer(strategy='median') | | preprocessor__num__std_scaler | StandardScaler() | | preprocessor__num__imputer__add_indicator | False | | preprocessor__num__imputer__copy | True | | preprocessor__num__imputer__fill_value | | | preprocessor__num__imputer__missing_values | nan | | preprocessor__num__imputer__strategy | median | | preprocessor__num__imputer__verbose | deprecated | | preprocessor__num__std_scaler__copy | True | | preprocessor__num__std_scaler__with_mean | True | | preprocessor__num__std_scaler__with_std | True | | preprocessor__cat__categories | auto | | preprocessor__cat__drop | | | preprocessor__cat__dtype | | | preprocessor__cat__handle_unknown | error | | preprocessor__cat__max_categories | | | preprocessor__cat__min_frequency | | | preprocessor__cat__sparse | True | | classifier__C | 1.0 | | classifier__class_weight | balanced | | classifier__dual | False | | classifier__fit_intercept | True | | classifier__intercept_scaling | 1 | | classifier__l1_ratio | | | classifier__max_iter | 300 | | classifier__multi_class | auto | | classifier__n_jobs | | | classifier__penalty | l2 | | classifier__random_state | | | classifier__solver | lbfgs | | classifier__tol | 0.0001 | | classifier__verbose | 0 | | classifier__warm_start | False |

### Model Plot The model plot is below.

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))])

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

## Evaluation Results You can find the details about evaluation process and the evaluation results. | Metric | Value | |----------|----------| | accuracy | 0.730305 | | f1 score | 0.730305 | # How to Get Started with the Model Use the code below to get started with the model.

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```python import pickle with open(dtc_pkl_filename, 'rb') as file: clf = pickle.load(file) ```

# Model Card Authors This model card is written by following authors: skops_user # 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:** ``` bibtex @inproceedings{...,year={2020}} ``` # Additional Content ## confusion_matrix ![confusion_matrix](confusion_matrix.png)