algorithm regression boosting with squared-error loss inputtraining set {(xi yi )}ni= the number of boosting rounds band shrinkage step-size parameter outputboosted prediction function - pn set (xn = yi for to do  (bn set (by ( for nand let - = fit prediction function hb on the training data tb set gb (xgb- (xg hb ( step-size parameter return gb the step-size parameter introduced in algorithm controls the speed of the fitting process specificallyfor small values of gboosting takes smaller steps towards the training loss minimization the step-size is of great practical importancesince it helps the boosting algorithm to avoid overfitting this phenomenon is demonstrated in figure train data - - - - train data - - figure the left and the right panels show the fitted boosting regression model with and respectively note the overfitting on the left very basic implementation of algorithm which reproduces figure is provided below regressionboosting py import numpy as np from sklearn tree import decisiontreeregressor from sklearn model_selection import train_test_split from sklearn datasets import make_regression import matplotlib pyplot as plt def trainboost (alpha boostingrounds , , )g_ np mean(