|
--- |
|
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. |
|
|
|
<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(), |
|
['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 | <class 'numpy.float64'> | |
|
| 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 | |
|
|
|
</details> |
|
|
|
### Model Plot |
|
|
|
The model plot is below. |
|
|
|
<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> |
|
|
|
## 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> |
|
|
|
```python |
|
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 |
|
|
|
 |
