README.md

metadata

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). See details there and use carefully.

Basic preprocessing done by the imodels team in this notebook.

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))