sklearn.metrics.mean_squared_error

sklearn.metrics.mean_squared_error(y_true, y_pred)

Mean squared error regression loss

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.

Returns :

loss : float

A positive floating point value (the best value is 0.0).

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 = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> mean_squared_error(y_true, y_pred)  
0.708...