sklearn.metrics.explained_variance_score

sklearn.metrics.explained_variance_score(y_true, y_pred, sample_weight=None)[source]

Explained variance regression score function

Best possible score is 1.0, lower values are worse.

Parameters:

y_true : array-like

Ground truth (correct) target values.

y_pred : array-like

Estimated target values.

sample_weight : array-like of shape = [n_samples], optional

Sample weights.

Returns:

score : float

The explained variance.

Notes

This is not a symmetric function.

Examples

>>> from sklearn.metrics import explained_variance_score
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> explained_variance_score(y_true, y_pred)  
0.957...