`Fairness.fairevaluate`

— Function`fairevaluate(classifiers, X, y; measures=nothing, measure=nothing, grp=:class, priv_grps, random_seed=12345, n_grps=6)`

Performed paired t-test for each pair of classifier in classifiers and return p values and t statistics.

**Arguments**

`classifiers`

: Array of classifiers to compare`X`

: DataFrame with features and protected attribute`y`

: Binary Target Variable`measures=nothing`

: The measures to be evaluated and used for HypothesisTests. If this is not specified, the`measure`

argument is used`measure=nothing`

: The performance/fairness measure used to perform hypothesis tests. If no values for measure is passed, then Disparate Impact will be used by default.`grp=:class`

: Protected Attribute Name`priv_grps=nothing`

: If default measure i.e. Disparate Impact is used, then pass an array of groups which are privileged in dataset.`random_seed=12345`

: Random seed to ensure reproducibility`n_grps=6`

: Number of folds for cross validation

**Returns**

A dictionary with following keys vs values is returned

`measures`

: names of the measures`classifier_names`

: names of the classifiers. If a pipeline is used, it will show pipeline and associated number.`results`

: 3-dimensional array with evaluation result. Its size is measures x classifiers x fold_number.`pvalues`

: 3-dimensional array with pvalues for each pair of classifier. Its size is measures x classifiers x classifiers.`tstats`

:3-dimensional array with tstats for each pair of classifier. Its size is measures x classifiers x classifiers.