In Machine Learning, the Random Forest Algorithm has supervised learning that can be used for both regression and classification but mainly used for classification. It is a type of bagging algorithm. In Random Forest, we start by taking random samples from the dataset. After that, it builds the decision tree for each sample, and then it will get the prediction from each decision tree. After this, voting will be performed for every predicted result. It returns the most voted prediction as a result.
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