d2_absolute_error_score#

sklearn.metrics.d2_absolute_error_score(y_true,y_pred,*,sample_weight=None,multioutput='uniform_average')[source]#

\(D^2\) regression score function, fraction of absolute error explained.

Best possible score is 1.0 and it can be negative (because the model can bearbitrarily worse). A model that always uses the empirical median ofy_trueas constant prediction, disregarding the input features,gets a\(D^2\) score of 0.0.

Read more in theUser Guide.

Added in version 1.1.

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’} or array-like of shape (n_outputs,), default=’uniform_average’

Defines aggregating of multiple output values.Array-like value defines weights used to average scores.

‘raw_values’ :

Returns a full set of errors in case of multioutput input.

‘uniform_average’ :

Scores of all outputs are averaged with uniform weight.

Returns:

scorefloat or ndarray of floats

The\(D^2\) score with an absolute error devianceor ndarray of scores if ‘multioutput’ is ‘raw_values’.

Notes

Like\(R^2\),\(D^2\) score may be negative(it need not actually be the square of a quantity D).

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

References

[1]

Eq. (3.11) of Hastie, Trevor J., Robert Tibshirani and Martin J.Wainwright. “Statistical Learning with Sparsity: The Lasso andGeneralizations.” (2015).https://hastie.su.domains/StatLearnSparsity/

Examples

>>>fromsklearn.metricsimportd2_absolute_error_score>>>y_true=[3,-0.5,2,7]>>>y_pred=[2.5,0.0,2,8]>>>d2_absolute_error_score(y_true,y_pred)0.764...>>>y_true=[[0.5,1],[-1,1],[7,-6]]>>>y_pred=[[0,2],[-1,2],[8,-5]]>>>d2_absolute_error_score(y_true,y_pred,multioutput='uniform_average')0.691...>>>d2_absolute_error_score(y_true,y_pred,multioutput='raw_values')array([0.8125    , 0.57142857])>>>y_true=[1,2,3]>>>y_pred=[1,2,3]>>>d2_absolute_error_score(y_true,y_pred)1.0>>>y_true=[1,2,3]>>>y_pred=[2,2,2]>>>d2_absolute_error_score(y_true,y_pred)0.0>>>y_true=[1,2,3]>>>y_pred=[3,2,1]>>>d2_absolute_error_score(y_true,y_pred)-1.0