Random forest: Is a machine learning

Random forest is a machine learning technique that creates multiple decision trees from random subsets of the training data and averages their predictions. It reduces the variance of the model and improves its accuracy, but also increases the bias and loses some interpretability. 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. Random Forest algorithm: Learn how this ensemble method boosts prediction accuracy by combining multiple decision trees for robust classification and regression.