explained_variance_score (original) (raw)
sklearn.metrics.explained_variance_score(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average', force_finite=True)[source]#
Explained variance regression score function.
Best possible score is 1.0, lower values are worse.
In the particular case when y_true is constant, the explained variance score is not finite: it is either NaN (perfect predictions) or-Inf (imperfect predictions). To prevent such non-finite numbers to pollute higher-level experiments such as a grid search cross-validation, by default these cases are replaced with 1.0 (perfect predictions) or 0.0 (imperfect predictions) respectively. If force_finiteis set to False, this score falls back on the original \(R^2\)definition.
Note
The Explained Variance score is similar to theR^2 score, with the notable difference that it does not account for systematic offsets in the prediction. Most often the R^2 score should be preferred.
Read more in the User Guide.
Parameters:
y_truearray-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_predarray-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
multioutput{‘raw_values’, ‘uniform_average’, ‘variance_weighted’} or array-like of shape (n_outputs,), default=’uniform_average’
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.
force_finitebool, default=True
Flag indicating if NaN and -Inf scores resulting from constant data should be replaced with real numbers (1.0 if prediction is perfect, 0.0 otherwise). Default is True, a convenient setting for hyperparameters’ search procedures (e.g. grid search cross-validation).
Added in version 1.1.
Returns:
scorefloat or ndarray of floats
The explained variance or ndarray if ‘multioutput’ is ‘raw_values’.
See also
Similar metric, but accounting for systematic offsets in prediction.
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... y_true = [-2, -2, -2] y_pred = [-2, -2, -2] explained_variance_score(y_true, y_pred) 1.0 explained_variance_score(y_true, y_pred, force_finite=False) nan y_true = [-2, -2, -2] y_pred = [-2, -2, -2 + 1e-8] explained_variance_score(y_true, y_pred) 0.0 explained_variance_score(y_true, y_pred, force_finite=False) -inf