| Summary: | 碩士 === 國立交通大學 === 統計學研究所 === 103 === Random Forest is a popular machine learning algorithms. It is a decision tree model consists of multiple trees. First, we generate a specified number of tree (ex: 100), then we predict the final result by taking average of all the results (for continuous response) or by majority voting of the results (for categorical response). Random forests in R software package “randomForest” is very easy to use. As long as we choose the number of the decision tree (ntry) and the number of variables to be selected for node branching (mtry), then we can analyze the data by this model. Its analysis results of the real data (Chapter 3) are better than some of the statistical model. What’s more, our model also has the ability for finding important variables. Therefore, it is a very complete and convenient model.
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