Random Forest is a popular supervised machine learning algorithm that can be used for both Classification and Regression problems in ML. In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than what could be obtained from any of the constituent learning algorithms alone. Random Forest is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and improve the performance of the model. 

As the name suggests, “Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset.” Instead of relying on one decision tree, the random forest takes the prediction from each tree, and based on the majority votes of predictions, it predicts the final output.

The greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting. The below diagram explains the working of the Random Forest algorithm:

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