suppose is training set with elements and talso of size nis obtained from by bootstrappingthat isresampling with replacement show that for large ntdoes not contain fraction of about - of the points from prove equation ( consider the following training/test split of the data construct random forest regressor and identify the optimal subset size in the sense of score (see remark import numpy as np from sklearn datasets import make_friedman from sklearn tree import decisiontreeregressor from sklearn model_selection import train_test_split from sklearn metrics import _score 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 = explain why bagging decision trees are special case of random forests show that ( holds consider the following classification data and module importsfrom sklearn datasets import make_blobs from sklearn metrics import zero_one_loss from sklearn model_selection import train_test_split import numpy as np import matplotlib pyplot as plt from sklearn ensemble import gradientboostingclassifier x_train y_train make_blobs n_samples = n_features = centers = random_state = cluster_std = using the gradient boosting algorithm with roundsplot the training loss as function of gfor what is your conclusion regarding the relation between and