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# sklearn.metrics.r2_score¶

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

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. 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

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...
```