sklearn.metrics.explained_variance_score

sklearn.metrics.explained_variance_score(y_true, y_pred, sample_weight=None, multioutput='uniform_average')

Explained variance regression score function

Best possible score is 1.0, lower values are worse.

Read more in the User Guide.

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.

sample_weight : array-like of shape = (n_samples), optional

Sample weights.

multioutput : string in [‘raw_values’, ‘uniform_average’, ‘variance_weighted’] or array-like of shape (n_outputs)

Defines aggregating of multiple output scores. Array-like value defines weights used to average scores.

‘raw_values’ :

Returns a full set of scores in case of multioutput input.

‘uniform_average’ :

Scores of all outputs are averaged with uniform weight.

‘variance_weighted’ :

Scores of all outputs are averaged, weighted by the variances of each individual output.

Returns:

score : float or ndarray of floats

The explained variance or ndarray if ‘multioutput’ is ‘raw_values’.

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)  # doctest: +ELLIPSIS
0.957...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> explained_variance_score(y_true, y_pred, multioutput='uniform_average')
... # doctest: +ELLIPSIS
0.983...