dask_ml.metrics.r2_score

dask_ml.metrics.r2_score

dask_ml.metrics.r2_score(y_true: dask_ml._typing.ArrayLike, y_pred: dask_ml._typing.ArrayLike, sample_weight: Optional[dask_ml._typing.ArrayLike] = None, multioutput: Optional[str] = 'uniform_average', compute: bool = True) dask_ml._typing.ArrayLike

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

This docstring was copied from sklearn.metrics.r2_score.

Some inconsistencies with the Dask version may exist.

Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

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’}, array-like of shape (n_outputs,) or None, default=’uniform_average’

Defines aggregating of multiple output scores. Array-like value defines weights used to average scores. Default is “uniform_average”.

‘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.

Changed in version 0.19: Default value of multioutput is ‘uniform_average’.

Returns
zfloat or ndarray of floats

The \(R^2\) score or ndarray of scores if ‘multioutput’ is ‘raw_values’.

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).

This metric is not well-defined for single samples and will return a NaN value if n_samples is less than two.

References

1

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,  
...          multioutput='variance_weighted')
0.938...
>>> y_true = [1, 2, 3]  
>>> y_pred = [1, 2, 3]  
>>> r2_score(y_true, y_pred)  
1.0
>>> y_true = [1, 2, 3]  
>>> y_pred = [2, 2, 2]  
>>> r2_score(y_true, y_pred)  
0.0
>>> y_true = [1, 2, 3]  
>>> y_pred = [3, 2, 1]  
>>> r2_score(y_true, y_pred)  
-3.0