create regression problem n_points points xy make_friedman n_samples =n_points n_features = noise = random_state = split to train /test set x_train x_test y_train y_test train_test_split (xytest_size = random_state = boosting sklearn from sklearn ensemble import gradientboostingregressor breg gradientboostingregressor learning_rate = n_estimators = max_depth = random_state = breg fit(x_train y_train yhat breg predict x_test print (gradient boosting ^ score ", _score (y_test yhat)gradient boosting ^ score we proceed with the classification setting and consider the original boosting algorithmadaboost the inventors of the adaboost method considered binary classification problemwhere the response variable belongs to the {- set the idea of adaboost is similar to the one presented in the regression settingthat isadaboost fits sequence of prediction functions with final prediction function gb (xg (xb hb ( )( = where each function hb is of the form hb (xab cb ( )with ab rand where cb is proper (but weakclassifier in some class thuscb ( {- exactly as in ( )we solve at each boosting iteration the optimization problem loss (yi gb- (xi (xi )(ab cb argmin > cc = ( howeverin this case the loss function is defined as loss( , ye-yby the algorithm starts with simple model : and for each successive iteration solves ( thusn gb- (xi -yi (xi -yi (xi (ab cb argmin |-yi{ argmin (bi > cc > cc = = wi(bwhere (bi :exp{-yi gb- (xi )does not depend on or it follows that - (ab cb argmin > cc = (bi { (xi yi - argmin ( > cc ` ( (ce- = (bi { (xi yi ( adaboost