| --- |
| library_name: sklearn |
| tags: |
| - sklearn |
| - skops |
| - tabular-classification |
| model_format: pickle |
| model_file: skops-89qohtne.pkl |
| widget: |
| - structuredData: |
| AGE: |
| - 32 |
| - 45 |
| - 25 |
| GENDER: |
| - m |
| - f |
| - f |
| HOWPAID: |
| - 'weekly ' |
| - 'weekly ' |
| - 'weekly ' |
| INCOME: |
| - 21772 |
| - 27553 |
| - 23477 |
| LOANS: |
| - 1 |
| - 2 |
| - 1 |
| MARITAL: |
| - 'married ' |
| - divsepwid |
| - 'single ' |
| MORTGAGE: |
| - y |
| - y |
| - n |
| NUMCARDS: |
| - 2 |
| - 6 |
| - 1 |
| NUMKIDS: |
| - 1 |
| - 4 |
| - 1 |
| STORECAR: |
| - 3 |
| - 5 |
| - 2 |
| --- |
| |
| # Model description |
|
|
| This is a logistic regression model trained on customers' credit card risk data in a bank using sklearn library. |
| The model predicts whether a customer is worth issuing a credit card or not. The full dataset can be viewed at the following link: https://huggingface.co/datasets/saifhmb/CreditCardRisk |
|
|
|
|
|
|
| ## Training Procedure |
|
|
| The data preprocessing steps applied include the following: |
| - Dropping high cardinality features, specifically ID |
| - Transforming and Encoding categorical features namely: GENDER, MARITAL, HOWPAID, MORTGAGE and the target variable, RISK |
| - Splitting the dataset into training/test set using 85/15 split ratio |
| - Applying feature scaling on all features |
| |
| ### Hyperparameters |
|
|
| <details> |
| <summary> Click to expand </summary> |
|
|
| | Hyperparameter | Value | |
| |-------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | memory | | |
| | steps | [('preprocessor', ColumnTransformer(remainder='passthrough',<br /> transformers=[('cat',<br /> Pipeline(steps=[('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']),<br /> ('num',<br /> Pipeline(steps=[('scale', StandardScaler())]),<br /> Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))])), ('classifier', LogisticRegression())] | |
| | verbose | False | |
| | preprocessor | ColumnTransformer(remainder='passthrough',<br /> transformers=[('cat',<br /> Pipeline(steps=[('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']),<br /> ('num',<br /> Pipeline(steps=[('scale', StandardScaler())]),<br /> Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))]) | |
| | classifier | LogisticRegression() | |
| | preprocessor__n_jobs | | |
| | preprocessor__remainder | passthrough | |
| | preprocessor__sparse_threshold | 0.3 | |
| | preprocessor__transformer_weights | | |
| | preprocessor__transformers | [('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']), ('num', Pipeline(steps=[('scale', StandardScaler())]), Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))] | |
| | preprocessor__verbose | False | |
| | preprocessor__verbose_feature_names_out | True | |
| | preprocessor__cat | Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]) | |
| | preprocessor__num | Pipeline(steps=[('scale', StandardScaler())]) | |
| | preprocessor__cat__memory | | |
| | preprocessor__cat__steps | [('onehot', OneHotEncoder(handle_unknown='ignore'))] | |
| | preprocessor__cat__verbose | False | |
| | preprocessor__cat__onehot | OneHotEncoder(handle_unknown='ignore') | |
| | preprocessor__cat__onehot__categories | auto | |
| | preprocessor__cat__onehot__drop | | |
| | preprocessor__cat__onehot__dtype | <class 'numpy.float64'> | |
| | preprocessor__cat__onehot__handle_unknown | ignore | |
| | preprocessor__cat__onehot__max_categories | | |
| | preprocessor__cat__onehot__min_frequency | | |
| | preprocessor__cat__onehot__sparse | deprecated | |
| | preprocessor__cat__onehot__sparse_output | True | |
| | preprocessor__num__memory | | |
| | preprocessor__num__steps | [('scale', StandardScaler())] | |
| | preprocessor__num__verbose | False | |
| | preprocessor__num__scale | StandardScaler() | |
| | preprocessor__num__scale__copy | True | |
| | preprocessor__num__scale__with_mean | True | |
| | preprocessor__num__scale__with_std | True | |
| | classifier__C | 1.0 | |
| | classifier__class_weight | | |
| | classifier__dual | False | |
| | classifier__fit_intercept | True | |
| | classifier__intercept_scaling | 1 | |
| | classifier__l1_ratio | | |
| | classifier__max_iter | 100 | |
| | 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 |
| |
| <style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 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-13 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 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-13 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-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 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-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 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-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 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-13 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-13" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('cat',Pipeline(steps=[('onehot',OneHotEncoder(handle_unknown='ignore'))]),['GENDER', 'MARITAL','HOWPAID', 'MORTGAGE']),('num',Pipeline(steps=[('scale',StandardScaler())]),Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))])),('classifier', LogisticRegression())])</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-97" type="checkbox" ><label for="sk-estimator-id-97" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('cat',Pipeline(steps=[('onehot',OneHotEncoder(handle_unknown='ignore'))]),['GENDER', 'MARITAL','HOWPAID', 'MORTGAGE']),('num',Pipeline(steps=[('scale',StandardScaler())]),Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))])),('classifier', LogisticRegression())])</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-98" type="checkbox" ><label for="sk-estimator-id-98" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder='passthrough',transformers=[('cat',Pipeline(steps=[('onehot',OneHotEncoder(handle_unknown='ignore'))]),['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']),('num',Pipeline(steps=[('scale', StandardScaler())]),Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object'))])</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-99" type="checkbox" ><label for="sk-estimator-id-99" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['GENDER', 'MARITAL', 'HOWPAID', 'MORTGAGE']</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-100" type="checkbox" ><label for="sk-estimator-id-100" 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-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-101" type="checkbox" ><label for="sk-estimator-id-101" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>Index(['AGE', 'INCOME', 'NUMKIDS', 'NUMCARDS', 'STORECAR', 'LOANS'], dtype='object')</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-102" type="checkbox" ><label for="sk-estimator-id-102" 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-103" type="checkbox" ><label for="sk-estimator-id-103" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[]</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-104" type="checkbox" ><label for="sk-estimator-id-104" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</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-105" type="checkbox" ><label for="sk-estimator-id-105" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div> |
| |
| ## Evaluation Results |
| - The target variable, RISK is multiclass. In sklearn, precision and recall functions have a parameter called, |
| average. This parameter is required for a multiclass/multilabel target. average = 'micro' was used to calculate |
| the precision and recall metrics globally by counting the total true positives, false negatives and false positives |
| |
| | Metric | Value | |
| |-----------|----------| |
| | accuracy | 0.699187 | |
| | precision | 0.699187 | |
| | recall | 0.699187 | |
| |
| ### Model Explainability |
| SHAP was used to determine the important features that helps the model make decisions |
|  |
| |
| ### Confusion Matrix |
| |
|  |
| |
| |
| |
| # Model Card Authors |
| |
| This model card is written by following authors: Seifullah Bello |
| |
| # Model Card Contact |
| |
| You can contact the model card authors through following channels: saifhmb@gmail.com |
| |
| |
