pycaret.classification.check_fairness

pycaret.classification.check_fairness(estimator, sensitive_features: list, plot_kwargs: dict = {})

There are many approaches to conceptualizing fairness. This function follows the approach known as group fairness, which asks: Which groups of individuals are at risk for experiencing harms. This function provides fairness-related metrics between different groups (also called subpopulation).

Example

>>> from pycaret.datasets import get_data
>>> income = get_data('income')
>>> from pycaret.classification import *
>>> exp_name = setup(data = income,  target = 'income >50K')
>>> lr = create_model('lr')
>>> lr_fairness = check_fairness(lr, sensitive_features = ['sex', 'race'])

estimator: scikit-learn compatible object

Trained model object

sensitive_features: list

List of column names as present in the original dataset before any transformations.

plot_kwargs: dict, default = {} (empty dict)

Dictionary of arguments passed to the matplotlib plot.

Returns:

pandas.DataFrame