Mean Squared Error#
A scale-dependent regression metric that punishes bad predictions more the worse they are. Formally, MSE is the average of the squared differences between a set of predictions and their target labels. For an unbiased estimator, the MSE measures the variance of the predictions.
Note: In order to maintain the convention of maximizing validation scores, this metric outputs the negative of the original score.
Estimator Compatibility: Regressor
Output Range: -∞ to 0
use Rubix\ML\CrossValidation\Metrics\MeanSquaredError; $metric = new MeanSquaredError();