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.
<details> <summary> Click to expand </summary>
| Hyperparameter | Value |
|---|---|
| memory | |
| 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))] |
| verbose | False | | 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) | | 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(handle_unknown='ignore'), ['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(handle_unknown='ignore') | | 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 | <class 'numpy.float64'> | | preprocessor__cat__handle_unknown | ignore | | 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 |
</details>
Model Plot
The model plot is below.
<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-5" 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(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>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-26" type="checkbox" ><label for="sk-estimator-id-26" 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="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" 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="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" 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-29" type="checkbox" ><label for="sk-estimator-id-29" 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-30" type="checkbox" ><label for="sk-estimator-id-30" 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-31" type="checkbox" ><label for="sk-estimator-id-31" 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-32" type="checkbox" ><label for="sk-estimator-id-32" 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="sk-estimator-id-33" type="checkbox" ><label for="sk-estimator-id-33" 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>
## 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.
<details> <summary> Click to expand </summary>
import pickle
with open(dtc_pkl_filename, 'rb') as file:
clf = pickle.load(file)
</details>
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
