Random Forest The random forest algorithm is one of the most popular and most powerful supervised machine learning algorithm in the modern world. It is capable of doing both regression and classification tasks. It develops lots of decision trees based on random selection of data and random selection of variables. It provides the class of dependent variable based on many trees. As the trees are based on random selection of data as well as variables,these are random tree. Many such random trees leads to a random forest. Advantages of random forest: 1. Can be used for both classification and the regression task. 2. Random forest classifier will handle the missing values and maintains accuracy for missing data. 3. It won't overfit the model. 4. Can handle large data set with higher dimensionality. There are some disadvantages as well. Let's look at some of them: 1. It's not as good as for regression as it does not give precise continuous nature prediction