[source] # 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. $$ {\displaystyle \operatorname {MSE} = {\frac {1}{n}}\sum _{i=1}^{n}(Y_{i}-{\hat {Y_{i}}})^{2}} $$ !!! note In order to maintain the convention of *maximizing* validation scores, this metric outputs the negative of the original score. **Estimator Compatibility:** Regressor **Score Range:** -∞ to 0 ## Parameters This metric does not have any parameters. ## Example ```php use Rubix\ML\CrossValidation\Metrics\MeanSquaredError; $metric = new MeanSquaredError(); ```