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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

Example#

use Rubix\ML\CrossValidation\Metrics\MeanSquaredError;

$metric = new MeanSquaredError();