The random forest algorithm is a supervised learning model. It uses labeled data to learn how to classify unlabeled data [1]. It is a sort of extension of the decision tree. The random forest has nearly the same hyperparameters as a decision tree or a bagging classifier. Random forest adds additional randomness to the model while growing the trees. Instead of searching for the most important feature while splitting a node, it searches for the best feature among a random subset of features [2].

We construct multiple datasets using the same set of the original dataset but with combinations of records where the duplicate record entry is allowed. This newly created dataset allowing repetition of tuples from original sets is known as a bootstrapped dataset.

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