the step-size parameter ab found by the adaboost algorithm in line can be viewed as an optimal step-size in the sense of training loss minimization howeversimilar to the regression settingone can slow down the adaboost algorithm by setting ab to be fixed (smallvalue ab as usualwhen the latter is done in practiceit is tackling the problem of overfitting we consider an implementation of algorithm for binary classification problem specificallyduring all boosting roundswe use simple decision trees of depth (also called decision tree stumpsas weak learners the exponential and zero-one training losses as function of the number of boosting rounds are presented in figure adaboost py from sklearn datasets import make_blobs from sklearn tree import decisiontreeclassifier from sklearn model_selection import train_test_split from sklearn metrics import zero_one_loss import numpy as np def exponentialloss ( ,yhat) len(yloss for in range ( )loss loss+np exp(- [ ]yhat[ ]loss loss/ return loss create binary classification problem np random seed ( n_points points xy make_blobs n_samples =n_points n_features = centers = cluster_std = random_state = [ == ]- adaboost implementation boostingrounds len(xw / *np ones(nlearner [alpha_b_arr [for in range boostingrounds )clf decisiontreeclassifier max_depth = clf fit( ,ysample_weight =wlearner append (clftrain_pred clf predict (xerr_b stumps