STAT 154 Lecture Notes - Lecture 23: Random Forest, Xz
10 Nov 2018
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Document Summary
Interpretation when we set the class with of classification , the classes become. I the measures the variation of the purity at the split. Ii ) hit: ink hogtie ping pitting pi. Taking many training set of training each data training set. The data c bootstrapping ) from the original. , building a separate prediction sample random with a selected as split candidate model using of d m from set is of predictors allowed to and average the result use only one of those m predictions selected. = e c it bagging ) = en if ree ) all. "t eh ftree , i the trees are independent when. B samples independent ? from the original data. V cx d= r then var c ts xi ) - = # ( 2 fr "t b o. Pr c " -75 fr "t the total.
