Random Forest Introduction Random forest is one of popular algorithm which is used for classification and regression as an ensemble learning. It means random forest includes multiple decision trees which the average of the result of each decision tree would be the final outcome for random forest. There are some drawbacks in decision tree such as over fitting on training set which causes high variance, although it was solved in random forest by the aid of Bagging (Bootstrap Aggregating). Now firstly it is better to pay attention to decision tree algorithm and then study about random forest. Because random forest is divided to multitude decision tree. Decision Tree: Decision tree uses tree-like graph to take as best as possible decision by considering all elements of graph. For instance, remember tennis player who has agenda to play in different weather conditions. And now we want to know if player will play on 15th day or not? Finding Pure Branch There are 15 days which in...