skfp.metrics.multioutput_recall_score#

skfp.metrics.multioutput_recall_score(y_true: ndarray, y_pred: ndarray, *args, **kwargs) → float#

Recall score for multioutput problems.

Returns the average value over all tasks. Missing values in target labels are ignored. Also supports single-task evaluation.

Any additional arguments are passed to the underlying recall_score function, see scikit-learn documentation for more information.

Parameters:

y_true (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Ground truth (correct) target values.
y_pred (array-like of shape (n_samples,) or (n_samples, n_outputs)) – Estimated target values.
*args – Any additional parameters for the underlying scikit-learn metric function.
**kwargs – Any additional parameters for the underlying scikit-learn metric function.

Returns:

score – Average recall value over all tasks.

Return type:

float

Examples

>>> import numpy as np
>>> from skfp.metrics import multioutput_recall_score
>>> y_true = [[0, 0], [1, 1]]
>>> y_pred = [[0, 0], [0, 1]]
>>> multioutput_recall_score(y_true, y_pred)
0.5
>>> y_true = [[0, np.nan], [1, np.nan], [np.nan, np.nan]]
>>> y_pred = [[0, 0], [0, 0], [1, 0]]
>>> multioutput_recall_score(y_true, y_pred)
0.0