Random forest creates a number of decision tress. The decision trees are created depending on the random selection of data and also the selection of variables randomly. The class of the dependent variable is determined by the class based on many decision trees. The major beliefs of random forest algorithm being most of the decision trees in the random forest predict the correct classes for most of the given data and the trees do not predict correctly at different places. The voting for each observation can be done and the class of the observation can be determined on the basis of the results of the voting. This voting and classification is considered to be much nearer to the exact classification. The ensemble learning method Random Forest (RF), ranks the importance of features in classification using the permutation scores for each rank. These ranks are generated in Random Forest method.
