Four algorithms out of 42 tested in a preliminary screening were selected (see next section for the selection process). Two algorithms (Extra Trees and Random Forests) are based on Regression Trees, which are a Decision Trees Regressor 94 used for regression tasks with the purpose of predicting output values. The other two, Gradient Boosting and AdaBoost (Adaptive Boosting), are based on the concept of boosting, which is an ensemble method combining several weak learners (base methods) into a strong learner. Boosting methods train predictors (predicted target features) sequentially, each trying to correct its predecessors.