Machine Learning Basics: Random Forest Regression

Previously, I had explained the various Regression models such as Linear, Polynomial, Support Vector and Decision Tree Regression. In this article, we will go through the code for the application of Random Forest Regression which is an extension to the Decision Tree Regression implemented previously. The Decision Tree is an easily understood and interpreted algorithm and hence a single tree may not be enough for the model to learn the features from it. On the other hand, Random Forest is also a "Tree"-based algorithm that uses the qualities features of multiple Decision Trees for making decisions. Therefore, it can be referred to as a'Forest' of trees and hence the name "Random Forest".