fairlearn.reductions.ErrorRate

class fairlearn.reductions.ErrorRate(*, costs=None)

Misclassification error as a moment.

A classifier \(h(X)\) has the misclassification error equal to

\[P[h(X) \ne Y]\]

It is also possible to specify costs for false positives and false negatives. The error then evaluates to

\[c_{FP} P[h(X)=1, Y=0] + c_{FN} P[h(X)=0, Y=1]\]

where \(c_{FP}\) and \(c_{FN}\) are the costs of false positive and false negative errors respectively. The standard misclassification error corresponds to \(c_{FP}=c_{FN}=1.0\).

Parameters:

costs (dict) – The dictionary with keys 'fp' and 'fn' containing the costs of false positives and false negatives. If none are provided costs of 1.0 are assumed.

Attributes:

total_samples

Return the number of samples in the data.

Methods

bound()	Return vector of fairness bound constraint the length of gamma.
gamma(predictor)	Return the gamma values for the given predictor.
load_data(X, y, *, sensitive_features[, ...])	Load the specified data into the object.
project_lambda(lambda_vec)	Return the lambda values.
signed_weights([lambda_vec])	Return the signed weights.

bound()

Return vector of fairness bound constraint the length of gamma.

gamma(predictor)

Return the gamma values for the given predictor.

Return type:

Series

load_data(X, y, *, sensitive_features, control_features=None)

Load the specified data into the object.

project_lambda(lambda_vec)

Return the lambda values.

signed_weights(lambda_vec=None)

Return the signed weights.

short_name = 'Err'

property total_samples

Return the number of samples in the data.