The understanding of decision tree and random forest is given. This paper mainly analyzes the learning algorithm of decision tree: information gain and ID3, C4.5, CART tree, and then give the random forest. Among the decision trees, there are 3 of the most important issues. 1. feature selection. Which is to choose which feature as the classification of a node; 2. the selection of eigenvalues. That is, how to divide the subtrees after the selection of the good features. 3. how to do the fitting of the decision tree? The following questions are explained on the decision tree respectively. The decision tree is often the optimal feature of the recursive selection, and the training data are segmented according to the feature.

Filename	Size	Update
决策树与随机森林\Class_8.pdf	636911	2017-09-14
决策树与随机森林\Class_8_Code.rar	992903	2017-09-14
决策树与随机森林\MacOS_precompiled-WITHOUT_SOURCE-v0.02.tar	358400	2017-09-14
决策树与随机森林\Windows-Precompiled-RF_MexStandalone-v0.02-.zip	455968	2017-09-14
决策树与随机森林	0	2018-01-26

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