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ISLP CH - 8 Tree Based Methods

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ISLP book training

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Yukutman

United States

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ISLP CH - 8 Tree Based Methods


ISLP CH - 8 Tree Based MethodsVersion en ligne ISLP book training par Yukutman 1 Random forests use randomly chosen subsets of size "m" from the predictor space having "p" number of predictors in their data set. Yes No 2 Bagging can be explained as Random Forests with "m = p". Where "m" is the subset size and "p" is the number of total predictors in the chosen data set. Yes No 3 Random Forests help to reduce overreliance on one strong predictor. Yes No 4 Is BART similar to boosting in that it iteratively fits trees to residuals, but differs by sampling trees via Bayesian MCMC under priors rather than using greedy stagewise optimization? Yes No 5 Burn-in method is a method used in BART to discard first "A" trees out of evaluation because they might have performed poorly. Yes No 6 Boosting and BART does not directly use original dataset when forming trees Yes No 7 Deep, unpruned trees have high variance, low bias. Yes No 8 In Bagging, large number of trees cause overfitting. Yes No