sklearn.metrics.explained_variance_score

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

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)  
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')
... 
0.983...