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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? :
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Returns a full set of scores in case of multioutput input.
- ?uniform_average? :
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Scores of all outputs are averaged with uniform weight.
- ?variance_weighted? :
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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...
sklearn.metrics.explained_variance_score()
2017-01-15 04:26:21
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