dask_ml.metrics.mean_squared_error¶

dask_ml.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average', compute=True)¶

Mean squared 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: A non-negative floating point value (the best value is 0.0), or an array of floating point values, one for each individual target.

Examples

>>> from sklearn.metrics import mean_squared_error  # doctest: +SKIP
>>> y_true = [3, -0.5, 2, 7]  # doctest: +SKIP
>>> y_pred = [2.5, 0.0, 2, 8]  # doctest: +SKIP
>>> mean_squared_error(y_true, y_pred)  # doctest: +SKIP
0.375
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]  # doctest: +SKIP
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]  # doctest: +SKIP
>>> mean_squared_error(y_true, y_pred)  # doctest: +ELLIPSIS, +SKIP
0.708...
>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')  # doctest: +SKIP
... # doctest: +ELLIPSIS
array([0.41666667, 1.        ])
>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])  # doctest: +SKIP
... # doctest: +ELLIPSIS
0.825...