Random forest
Random forest is one of the best tree-based methods. Random forest is an ensemble of decision trees and each decision tree has certain weights associated with it. A decision of the random forest is decided like voting, as the majority of decision tree outcomes decide the outcome of the random forest. So we start using the randomForest package and this can be installed and loaded using the following commands:
>install.packages("randomForest") >library(randomForest)
We can also use the following command to know more about this randomForest package, including version, date of release, URL, set of functions implemented in this package, and much more:
>library(help=randomForest)
Random forest works best for any type of problem and handles classification, regression, and unsupervised problems quite well. Depending upon the type of labeled variable, it will implement relevant decision trees; for example, it uses classification for factor target variables, regression for numeric...
