The larger the difference between RMSE and MAE, the greater the likelihood of predicting records with substantial errors, as the RMSE overemphasizes outliers. To enhance the predictability of the model, it is crucial to identify and eliminate (i) noise within the feature space (irrelevant features) and (ii) noise in the records (outliers in particular). We explored the former using Gini importance and addressed the lat-ter by removing outliers from the datasets through an iterative protocol, which we will discuss in detail later.