aif360.sklearn.metrics.smoothed_selection_rate

aif360.sklearn.metrics.smoothed_selection_rate(y_true, y_pred, *, concentration=1.0, pos_label=1, sample_weight=None)[source]

Compute the smoothed selection rate, \(\frac{TP + FP + \alpha}{P + N + |R_Y|\alpha}\).

Parameters:

  • y_true (array-like) – Ground truth (correct) target values. Ignored.

  • y_pred (array-like) – Estimated targets as returned by a classifier.

  • concentration (scalar) – Dirichlet smoothing concentration parameter \(|R_Y|\alpha\) (must be non-negative).

  • pos_label (scalar, optional) – The label of the positive class.

  • sample_weight (array-like, optional) – Sample weights.

Returns:

float – Smoothed selection rate.