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README.md

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----
-annotations_creators: []
-language: []
-language_creators: []
-license: []
-multilinguality: []
-pretty_name: credit-card
-size_categories:
-- 10K<n<100K
-source_datasets: []
-tags:
-- interpretability
-- fairness
-- medicine
-task_categories:
-- tabular-classification
-task_ids: []
----
-
-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.
-
-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).
-
-The target is the binary outcome `default.payment.next.month`.
-
-### Sample usage
-
-Load the data:
-
-```
-from datasets import load_dataset
-
-dataset = load_dataset("imodels/credit-card")
-df = pd.DataFrame(dataset['train'])
-X = df.drop(columns=['default.payment.next.month'])
-y = df['default.payment.next.month'].values
-```
-
-Fit a model:
-
-```
-import imodels
-import numpy as np
-
-m = imodels.FIGSClassifier(max_rules=5)
-m.fit(X, y)
-print(m)
-```
-
-
-Evaluate:
-
-
-```
-df_test = pd.DataFrame(dataset['test'])
-X_test = df.drop(columns=['default.payment.next.month'])
-y_test = df['default.payment.next.month'].values
-print('accuracy', np.mean(m.predict(X_test) == y_test))
-```

imodels--credit-card/csv-test.parquet

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imodels--credit-card/csv-train.parquet

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