Random Forest is a machine learning algorithm that uses an ensemble of decision trees to make predictions. The algorithm was first introduced by Leo Breiman in 2001. In this post we’ll cover how the random forest algorithm works, how it differs from other algorithms and how to use it. What Is Random Forest ? Random forest is a supervised learning algorithm. The “forest” it builds is an ensemble of decision trees, usually trained with the bagging method. A random forest is an ensemble learning method that combines the predictions from multiple decision trees to produce a more accurate and stable prediction. It is a type of supervised learning algorithm that can be used for both classification and regression tasks. In regression task we can use Random Forest Regression technique for predicting numerical values. It predicts continuous values by averaging the results of multiple decision trees. Working of Random Forest Regression Random Forest ... Random Forest is a part of bagging (bootstrap aggregating) algorithm because it builds each tree using different random part of data and combines their answers together.
