coverforest.metrics.regression_coverage_score

coverforest.metrics.regression_coverage_score#

coverforest.metrics.regression_coverage_score(y_true, y_pred, *, sample_weight=None)[source]#

Compute the empirical coverage for regression prediction intervals.

The coverage score measures the proportion of true values that fall within the predicted intervals.

Parameters:

  • y_true (array-like of shape (n_samples,)) – Ground truth (correct) target values.

  • y_pred (tuple, list or array-like of shape (n_samples, 2)) – Predicted intervals, where each row contains [lower_bound, upper_bound].

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights. If None, then samples are equally weighted.

Returns:

score – Returns the empirical coverage, i.e., the proportion of true values falling within the prediction intervals, weighted by sample_weight. Best value is 1 and worst value is 0.

Return type:

float

Examples

>>> import numpy as np
>>> from metrics import regression_coverage_score
>>> y_true = [1.0, -2.0, 3.0]
>>> y_pred = np.array([[0.5, 1.5], [1.5, 2.5], [2.5, 3.5]])
>>> regression_coverage_score(y_true, y_pred)
0.66...