Depending on the goal whether to decrease bias error and overfitting, or to decrease both bias and variance, bagging and boosting are generally used, respectively. Concentrating in the bagging, unstable models, i.e., models whose performance is sensible to small perturbations of the training set, are trained with different replicas of the training set, obtained with replacement to keep the same number of examples (bootstrap aggregation). Then, new examples are predicted by uniform voting between the regressors trained with different dataset replicas. A special variant of bagging applied to decision tress results in the RF method, which operates by constructing multiple decision trees with bagging and random selection of features at each split of each tree. This mechanism assures that the constructed trees become correlated with one or more features which are strong predictors .