median_absolute_error (original) (raw)
sklearn.metrics.median_absolute_error(y_true, y_pred, *, multioutput='uniform_average', sample_weight=None)[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,), 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.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
Added in version 0.24.
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) np.float64(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) np.float64(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]) np.float64(0.85)