sklearn.metrics.mean_absolute_error — scikit-learn 0.20.4 documentation (original) (raw)
sklearn.metrics. mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average')[source]¶
Mean absolute error regression loss
Read more in the User Guide.
| Parameters: | y_true : array-like of shape = (n_samples) or (n_samples, n_outputs) Ground truth (correct) target values. y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs) Estimated target values. sample_weight : array-like of shape = (n_samples), optional Sample weights. multioutput : string in [‘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: | loss : float 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. |
Examples
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) 0.5 y_true = [[0.5, 1], [-1, 1], [7, -6]] y_pred = [[0, 2], [-1, 2], [8, -5]] mean_absolute_error(y_true, y_pred) 0.75 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]) ... 0.85...