Mean Squared Error#
A scale-dependent regression metric that gives greater weight to error scores the worse they are. Formally, Mean Squared Error (MSE) is the average of the squared differences between a set of predictions and their target labels.
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();