dask_ml.metrics.mean_absolute_error(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

Mean absolute error regression loss.

This docstring was copied from sklearn.metrics.mean_absolute_error.

Some inconsistencies with the Dask version may exist.

Read more in the User Guide.

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 errors.

‘raw_values’ :

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

‘uniform_average’ :

Errors of all outputs are averaged with uniform weight.

lossfloat or ndarray of floats

If multioutput is ‘raw_values’, then mean absolute error is returned for each output separately. If multioutput is ‘uniform_average’ or an ndarray of weights, then the weighted average of all output errors is returned.

MAE output is non-negative floating point. The best value is 0.0.


>>> from sklearn.metrics import mean_absolute_error  
>>> y_true = [3, -0.5, 2, 7]  
>>> y_pred = [2.5, 0.0, 2, 8]  
>>> mean_absolute_error(y_true, y_pred)  
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]  
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]  
>>> mean_absolute_error(y_true, y_pred)  
>>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')  
array([0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])