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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? :
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Returns a full set of errors in case of multioutput input.
- ?uniform_average? :
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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]) ... 0.849...
sklearn.metrics.mean_absolute_error()
2017-01-15 04:26:28
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