sklearn.metrics.r2_score

sklearn.metrics.r2_score(y_true, y_pred, sample_weight=None)

R^2 (coefficient of determination) regression score function.

Best possible score is 1.0, lower values are worse.

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.

Returns:

z : float

The R^2 score.

Notes

This is not a symmetric function.

Unlike most other scores, R^2 score may be negative (it need not actually be the square of a quantity R).

References

[R168]

Wikipedia entry on the Coefficient of determination

Examples

>>> from sklearn.metrics import r2_score
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> r2_score(y_true, y_pred)  
0.948...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> r2_score(y_true, y_pred)  
0.938...