pip
to install the Etiq library and its dependencies.protected
parameter; specifiy who is in the privileged and unprivileged groups (these are set using the privileged
and unprivileged
parameters respectively); specify what is the positive outcome and the negative outcome in this dataset (these are set using the positive_outcome_label
and negative_outcome_label
parameters respectively). accuracy
metric returns the fraction of the training dataset which is correctly classified. The equal_opportunity
metric measures the difference in true positive rate between a privileged demographic group and an unprivileged demographic group. The other available metrics used to evaluate model performance are accuracy
(proportion of outcomes correctly classified out of total outcomes)true_neg_rate
(the proportion negative outcome labels that are correctly classified out of all negative outcome labels) true_pos_rate
(the proportion positive outcome labels that are correct out of all positive outcome labels)
demographic_parity
(the difference between number of positive labels out of total from a privileged demographic group vs. a unprivileged demographic group)equal_odds_tpr & equal_odds_tnr
(unlike with equal_opportunity, this criteria looks at difference between true positive rate - privileged vs. unpriviledge and true negative rate - privileged vs. unprivileged, with the aim of ensuring that the difference for both metrics are minimal)DefaultXGBoostClassifier
(a wrapper around XGBoost classifier), DefaultRandomForestClassifier
(a wrapper around the random forest classifier from sklearn) and DefaultLogisticRegression
(a wrapper around the logistic regression classifier from sklearn).Model
class . We could, for example, create an LGBMClassifier model, train it and use the trained model.