coverforest.metrics.average_interval_length_loss

coverforest.metrics.average_interval_length_loss(y_true, y_pred)

Compute the average length of regression prediction intervals.

For each sample, the interval length is the difference between the upper and lower bounds.

Parameters:

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

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

Returns:

score – Returns the average interval length. Minimum possible value is 0, no maximum value.

Return type:

float

Examples

>>> import numpy as np
>>> from metrics import average_interval_length_loss
>>> y_pred = np.array([[0.5, 2.5], [1.5, 4.5], [2.5, 3.5]])
>>> average_interval_length_loss(y_pred)
2.0