Base learner implementation and parameters
For the simulation experiment, we tested three base learners:
ExtraTreeRegressor, Linear Regression and Support Vector Machines (SVM).
For ExtraTreesRegressor, we used 1000 trees with a maximal depth of 3,
squared error as the criterion of split, minimal number of examples as 2
in a split and minimal number of examples in a node as 1. For SVM, we
used the SVR (support vector regressor) implemented in sklearn, with
C=1.0, and epsilon = 0.2. For Linear Regression we used Ridge
penalization with alpha = 1.0.