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--- |
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annotations_creators: [] |
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language: [] |
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language_creators: [] |
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license: [] |
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multilinguality: [] |
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pretty_name: credit-card |
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size_categories: |
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- 10K<n<100K |
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source_datasets: [] |
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tags: |
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- interpretability |
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- fairness |
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- medicine |
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task_categories: |
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- tabular-classification |
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task_ids: [] |
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--- |
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Port of the credit-card dataset from UCI (link [here](https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset)). See details there and use carefully. |
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Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb). |
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The target is the binary outcome `default.payment.next.month`. |
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### Sample usage |
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Load the data: |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("imodels/credit-card") |
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df = pd.DataFrame(dataset['train']) |
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X = df.drop(columns=['default.payment.next.month']) |
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y = df['default.payment.next.month'].values |
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``` |
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Fit a model: |
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``` |
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import imodels |
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import numpy as np |
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m = imodels.FIGSClassifier(max_rules=5) |
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m.fit(X, y) |
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print(m) |
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``` |
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Evaluate: |
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``` |
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df_test = pd.DataFrame(dataset['test']) |
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X_test = df.drop(columns=['default.payment.next.month']) |
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y_test = df['default.payment.next.month'].values |
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print('accuracy', np.mean(m.predict(X_test) == y_test)) |
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``` |
