Random forest

Random forest is a popular machine learning algorithm that belongs to the category of ensemble learning methods, particularly bagging. It combines the predictions of multiple individual decision trees to make more accurate and robust predictions.

Random forest is a versatile algorithm that utilizes the power of decision trees. It constructs many decision trees and combines their outputs to obtain final predictions. Each decision tree is built using a random subset of the training data and a random subset of features. By averaging the predictions of these individual trees, Random forest can reduce overfitting and improve generalization.

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