Random Forests are a general class of ensemble building methods that use a decision tree as the base classifier. The Random Forest classifier is a variation of the Bagging classifier (Bootstrap Aggregating). The Bagging algorithm is a method of classification that generates weak individual classifiers using bootstrap. Each classifier is trained on a random redistribution of the training set so that many of the original examples may be repeated in each classification.

The principal difference between Bagging and Random Forest is that Bagging uses all the features in each tree node and Random Forest selects a random subset of the features. The suitable number of randomized features corresponds to the square root of the total number of features. For prediction, a new sample is pushed down the tree and it is assigned the class of the terminal (or leaf) node in the tree. This method is iterated over all the trees, and finally, the average vote of all the tree predictions...