aif360.algorithms.preprocessing
.DisparateImpactRemover¶
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class
aif360.algorithms.preprocessing.
DisparateImpactRemover
(repair_level=1.0, sensitive_attribute='')[source]¶ Disparate impact remover is a preprocessing technique that edits feature values increase group fairness while preserving rank-ordering within groups [1].
References
[1] M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian, “Certifying and removing disparate impact.” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015. Parameters: Methods
fit
Train a model on the input. fit_predict
Train a model on the input and predict the labels. fit_transform
Run a repairer on the non-protected features and return the transformed dataset. predict
Return a new dataset with labels predicted by running this Transformer on the input. transform
Return a new dataset generated by running this Transformer on the input. -
fit_transform
(dataset)[source]¶ Run a repairer on the non-protected features and return the transformed dataset.
Parameters: dataset (BinaryLabelDataset) – Dataset that needs repair. Returns: dataset (BinaryLabelDataset) – Transformed Dataset. Note
In order to transform test data in the same manner as training data, the distributions of attributes conditioned on the protected attribute must be the same.
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