fairlearn.datasets.fetch_adult#

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

Load the UCI Adult dataset (binary classification).

Read more in the User Guide.

New in version 0.5.0.

Parameters:

cachebool, default=True: Whether to cache downloaded datasets using joblib.
data_homestr, default=None: Specify another download and cache folder for the datasets. By default, all fairlearn data is stored in ‘~/.fairlearn-data’ subfolders.
as_framebool, default=True: If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target_columns. The Bunch will contain a frame attribute with the target and the data. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as describe above.

Changed in version 0.9.0: Default value changed to True.
return_X_ybool, default=False: If True, returns (data.data, data.target) instead of a Bunch object.

Returns:

datasetBunch

Dictionary-like object, with the following attributes.

datandarray, shape (48842, 14): Each row corresponding to the 14 feature values in order. If as_frame is True, data is a pandas object.
targetnumpy array of shape (48842,): Each value represents whether the person earns more than $50,000 a year (>50K) or not (<=50K). If as_frame is True, target is a pandas object.
feature_nameslist of length 14: Array of ordered feature names used in the dataset.
DESCRstring: Description of the UCI Adult dataset.
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.
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().

References

Gallery examples#