
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))
```
task_categories:tabular-classification size_categories:10K<n<100K interpretability fairness medicine region:us
{"annotations_creators": [], "language_creators": [], "language": [], "license": [], "multilinguality": [], "size_categories": ["10K<n<100K"], "source_datasets": [], "task_categories": ["tabular-classification"], "task_ids": [], "pretty_name": "credit-card", "tags": ["interpretability", "fairness", "medicine"]}
TAGS
#task_categories-tabular-classification #size_categories-10K<n<100K #interpretability #fairness #medicine #region-us 


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 'URL'.

### Sample usage

Load the data:



Fit a model:




Evaluate:



### Sample usage

Load the data:



Fit a model:




Evaluate:
TAGS
#task_categories-tabular-classification #size_categories-10K<n<100K #interpretability #fairness #medicine #region-us 
 ### Sample usage

Load the data:



Fit a model:




Evaluate: