dask_ml.metrics.mean_squared_error

dask_ml.metrics.mean_squared_error

dask_ml.metrics.mean_squared_error(y_true: dask_ml._typing.ArrayLike, y_pred: dask_ml._typing.ArrayLike, sample_weight: Optional[dask_ml._typing.ArrayLike] = None, multioutput: Optional[str] = 'uniform_average', squared: bool = True, compute: bool = True) dask_ml._typing.ArrayLike

Mean squared error regression loss.

This docstring was copied from sklearn.metrics.mean_squared_error.

Some inconsistencies with the Dask version may exist.

Read more in the User Guide.

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.

squaredbool, default=True

If True returns MSE value, if False returns RMSE value.

Returns
lossfloat 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  
>>> 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 = [3, -0.5, 2, 7]  
>>> y_pred = [2.5, 0.0, 2, 8]  
>>> mean_squared_error(y_true, y_pred, squared=False)  
0.612...
>>> 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, squared=False)  
0.822...
>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')  
array([0.41666667, 1.        ])
>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])  
0.825...