fairlearn.datasets.fetch_credit_card#

fairlearn.datasets.fetch_credit_card(*, cache=True, data_home=None, as_frame=True, return_X_y=False)[source]#

Load the ‘Default of Credit Card clients’ dataset (binary classification).

Samples total

30000

Dimensionality

23

Features

real

Classes

2

Source: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients I-Cheng Yeh and Che-hui Lien, “The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients”, Expert Systems with Applications, 36(2), 2473-2480, 2009

Added in version 0.9.0.

Parameters:

  • cache (bool, default=True) – Whether to cache downloaded datasets using joblib.

  • data_home (str or None, default=None) – Specify another download and cache folder for the datasets. By default, all fairlearn data is stored in ‘~/.fairlearn-data’ subfolders.

  • as_frame (bool, default=True) –

    If True,

    Returns the data as Pandas DataFrame, and the target is returned as a Pandas Series.

    If False,

    Returns a scikit-learn Bunch object with frame attribute containing the data and the target.

    Changed in version 0.9.0: Default value changed to True.

  • return_X_y (bool, default=False) –

    If True,

    returns (data.data, data.target)

    Else,

    return Sci-kit Learn Bunch object

Returns:

  • dataset (class:~sklearn.utils.Bunch) – Dictionary-like object, with the following attributes.

    dataNumPy Array or Pandas DataFrame, Shape (30000, 23)

    Each row corresponds to the 23 feature values in order. If as_frame is True, data is a Pandas DataFrame

    targetNumPy Array or Pandas Series, Shape (30000,)

    Each value represents whether an applicant defaulted on credit loan. If as_frame is True, target is a Pandas Series.

    feature_namesList of Strings, Length 23

    The ordered UCI column names: LIMIT_BAL, SEX, EDUCATION, MARRIAGE, AGE, PAY_0, PAY_2 … PAY_6, BILL_AMT1 … BILL_AMT6, PAY_AMT1 … PAY_AMT6. When as_frame is True the same names are used for the data and frame columns, and for the X returned by return_X_y=True. Each returned Bunch has its own feature_names list.

    Changed in version 0.15.0: Previously these were whatever names the underlying OpenML record carried, which are placeholders (x1 … x23) rather than the UCI column names.

    DESCRstring

    Description of the UCI Default of Credit Card

    categoriesdict or None

    Maps each categorical feature name to a list of values, such that the value encoded as i is ith in the list. If as_frame is True, this is None. The keys use the UCI names listed under feature_names.

    framepandas DataFrame

    Only present when as_frame is True. DataFrame with data and target.

  • (data, target) (tuple if return_X_y is True)

Notes

Our API largely follows the API of sklearn.datasets.fetch_openml().

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