Random forest is an extremely popular machine learning technique that is used mainly for classification and regression. As the algorithm builds multiple decision trees, we have already covered a substantial part of the foundation required for random forest. Let's quickly understand the algorithm and solve our previous problem better.
Random forest is a machine learning technique built on the principle of ensemble modeling. It builds an ensemble of decision trees with each tree having a randomly chosen subset of features; hence the name Random + Forest. Random forest is basically an advanced version of the bagging algorithm. In bagging, we build multiple decision trees with a bootstrapped training sample selected with replacement from the entire training set. In random forest, the addition of randomness is taken one step further. Here, from the entire list of features only a predefined number of features are chosen randomly for each tree...