where 𝑛 is the total number of samples, 𝑦 is the observed (target) value and 𝑦 S is the value estimated by the algorithm (model). In our case, 𝑦 is the experimental impact sensitivity value ℎ .# and 𝑦 S is the predicted value (target feature) predicted by each model. As it is well-known, the square of the difference (𝑦 -𝑦 S) penalizes the most significant deviations, especially outliers. The square of the RMSE is the mean square error (MSE) or variance. The mean absolute error (MAE), another measure of the error, is defined in the usual way as 1/𝑛 ∑ |𝑦 -𝑦 S| T UV*