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---
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: [email protected]
|