fetch_credit_fraud#

skrub.datasets.fetch_credit_fraud(data_home=None, split='train')[source]#

Fetch the credit fraud dataset (classification).

Available at https://github.com/skrub-data/skrub-data-files

This is an imbalanced binary classification use-case. This dataset consists of two tables:

  • baskets, containing the binary fraud target label

  • products

Baskets contain at least one product each, so aggregation then joining operations are required to build a design matrix. Size on disk: 16MB.

Parameters:
data_homestr or path-like, default=None

The directory where to download and unzip the files.

splitstr, default=”train”

The split to load. Can be either “train”, “test”, or “all”.

Returns:
bunchBunch

A dictionary-like object with the following keys:

basketsDataFrame of shape (92790, 2)

Table containing baskets ID and target.

productsDataFrame of shape (163357, 7)

Table containing features about products contained in baskets

metadatadict

A dictionary containing the name, description, source and target.

baskets_pathstr

The path to the baskets CSV file.

products_pathstr

The path to the products CSV file.