ISLP CH8 - Tree-Based Methods

Fill in the Blanks

(1)

Played 2

Create copy

Introduction to Statistical Learning with Python book

Yukutman

United States

#	Player	Accuracy	time
1	tachyon August 20, 2025	% 76	05:12

Do you want to stay in the Top 10 of this game? Log in to identify yourself.

Make your own free game from our game creator

Compete against your friends to see who gets the best score in this game

Make challenge


Fill in the Blanks ISLP CH8 - Tree-Based MethodsOnline version Introduction to Statistical Learning with Python book by Yukutman 1 While error is intuitive , it is rarely used for tree construction because it is less sensitive to changes in node class proportions compared to the index and . 2 Recursive binary splitting is a algorithm : it does not reconsider earlier splits once they are made . 3 In cost - complexity pruning , the complexity parameter ? penalizes the number of in the tree , encouraging simpler models . 4 Cross - validation is typically used to select the optimal value of for pruning . 5 In random forests , at each split only a of predictors is considered , which reduces correlation among the trees . 6 The typical number of predictors considered at each split in a random forest is ? p for and p / 3 for . 7 In boosting , each new tree is fit on the of the previous model , and the final prediction is obtained by a weighted of all trees . 8 The three key tuning parameters in boosting are the number of , the rate , and the maximum of each tree . 9 The key advantage of out - of - bag error is that it avoids the need for a separate set .