mean_squared_error

pai4sk.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average')

Mean squared error regression loss

For SnapML solver this supports both local and distributed(MPI) method of execution.

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. It also accepts SnapML data partition, which includes the correct labels.
• 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 – A non-negative floating point value (the best value is 0.0), or an array of floating point values, one for each individual target.

Return type:

float or ndarray of floats

Examples

>>> from pai4sk.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)  # doctest: +ELLIPSIS
0.708...
>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
... # doctest: +ELLIPSIS
array([0.41666667, 1.        ])
>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.825...