aif360.algorithms.inprocessing
.PrejudiceRemover
- class aif360.algorithms.inprocessing.PrejudiceRemover(eta=1.0, sensitive_attr='', class_attr='')[source]
Prejudice remover is an in-processing technique that adds a discrimination-aware regularization term to the learning objective [6].
References
- Parameters:
Methods
Learns the regularized logistic regression model.
fit_predict
Train a model on the input and predict the labels.
fit_transform
Train a model on the input and transform the dataset accordingly.
Obtain the predictions for the provided dataset using the learned prejudice remover model.
transform
Return a new dataset generated by running this Transformer on the input.
- fit(dataset)[source]
Learns the regularized logistic regression model.
- Parameters:
dataset (BinaryLabelDataset) – Dataset containing true labels.
- Returns:
PrejudiceRemover – Returns self.
- predict(dataset)[source]
Obtain the predictions for the provided dataset using the learned prejudice remover model.
- Parameters:
dataset (BinaryLabelDataset) – Dataset containing labels that needs to be transformed.
- Returns:
dataset (BinaryLabelDataset) – Transformed dataset.