root_mean_squared_error#
- sklearn.metrics.root_mean_squared_error(y_true,y_pred,*,sample_weight=None,multioutput='uniform_average')[source]#
Root mean squared error regression loss.
Read more in theUser Guide.
Added in version 1.4.
- 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.
- 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.
- Returns:
- lossfloat or ndarray of floats
A non-negative floating point value (the best value is 0.0), or anarray of floating point values, one for each individual target.
Examples
>>>fromsklearn.metricsimportroot_mean_squared_error>>>y_true=[3,-0.5,2,7]>>>y_pred=[2.5,0.0,2,8]>>>root_mean_squared_error(y_true,y_pred)0.612...>>>y_true=[[0.5,1],[-1,1],[7,-6]]>>>y_pred=[[0,2],[-1,2],[8,-5]]>>>root_mean_squared_error(y_true,y_pred)0.822...
Gallery examples#
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