sklearn.metrics.
mean_squared_error
(y_true, y_pred, sample_weight=None, multioutput='uniform_average')[source]¶Mean squared error regression loss
Read more in the User Guide.
or array-like of shape (n_outputs) Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
>>> from sklearn.metrics import mean_squared_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_squared_error(y_true, y_pred)
0.375
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> mean_squared_error(y_true, y_pred)
0.708...
>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
...
array([ 0.416..., 1. ])
>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
...
0.824...