Create README.md

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))
+```