Random forest: Is a part of bagging (bootstrap

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 is a commonly-used machine learning algorithm , trademarked by Leo Breiman and Adele Cutler, that combines the output of multiple decision trees to reach a single result. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems. 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 forests analyze weather patterns, soil conditions, and crop data to improve farming practices and maximize yield. Advantages and Disadvantages of Random Forest Advantages High Accuracy: Random Forest usually provides higher accuracy than individual decision trees because it combines the predictions of multiple trees.