sklearn.metrics.median_absolute_error¶
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sklearn.metrics.median_absolute_error(y_true, y_pred, multioutput='uniform_average')[source]¶ Median absolute error regression loss
Median absolute error output is non-negative floating point. The best value is 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.
- multioutput{‘raw_values’, ‘uniform_average’} or array-like of shape
(n_outputs,) 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.
- Returns
- 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.
Examples
>>> from sklearn.metrics import median_absolute_error >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> median_absolute_error(y_true, y_pred) 0.5 >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred = [[0, 2], [-1, 2], [8, -5]] >>> median_absolute_error(y_true, y_pred) 0.75 >>> median_absolute_error(y_true, y_pred, multioutput='raw_values') array([0.5, 1. ]) >>> median_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7]) 0.85
