In the previous session, you learnt how to use decision trees for regression analysis. However, you know that decision trees have their own limitations, and you need to overcome them to use random forests in order to exploit the predictive power of decision trees and obtain better results. Let’s quickly understand how random forest regression is performed.
So now, you have a good understanding of how decision trees and random forests can be used for decision-making whenever you have continuous target variables. You also learnt how to explore the feature importance that is offered by both these models. As an exercise, you can definitely perform more hyperparameter tuning here and improve the performance of the model that you built right now.