sklearn.metrics.mean_squared_error¶
- sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None)[source]¶
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.
sample_weight : array-like of shape = [n_samples], optional
Sample weights.
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...
