Titlebook: Random Forests with R; Robin Genuer,Jean-Michel Poggi Book 2020 Springer Nature Switzerland AG 2020 Random forests.Machine learning.Classi

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Variable Importance,nce of groups of correlated variables. Then, its behavior with regard to random forest parameters is addressed. In the final section, the use of variable importance is first illustrated by simulation in regression, and then in three examples: predicting ozone concentration, analyzing genomic data, and determining the local level of dust pollution.

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CART,for both regression and classification problems. This chapter focuses on CART trees, analyzing in detail the two steps involved in their construction: the maximal tree growing algorithm, which produces a large family of models, and the pruning algorithm, which is used to select an optimal or suitabl

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annexation

Random Forests,ain parameters: the number of trees and the number of variables picked at each node. In the final section, random forests are applied to three examples: predicting ozone concentration, analyzing genomic data, and analyzing dust pollution.

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