![]() In this technique a generalized result is obtained by combining the results of various predictive models. The size of the subsets used is the same as that of the original set. ![]() This technique is also referred to as Bagging. Image from O’Reilly Media Bootstrap Aggregationīootstrapping is a sampling technique in which we create subsets of observations from the original dataset. It’s highly likely that you will go for the second option and instead of making a direct conclusion you will consider other options as well. Will you just log in to your account and watch the first webisode that pops up or will you browse a few web pages, compare the ratings and then make a decision. Suppose you want to watch a web series on Netflix. This algorithm can be used for computing both Regression and classification problems.įirst, we will get acquainted with some important terms like Ensemble Learning and Bootstrap Aggregation, then I will try to give you an intuition of the algorithm and finally, we will build our very own machine learning model using the Random forest regressor.īefore starting this article, I would recommend you to take a look on my previous article on Decision trees here which is a crucial prerequisite for anyone who is looking forward to learning this algorithm. In this article I will try to give you an intuition of the concepts of how a Random Forest model works from scratch. ![]() A hands on approach on Random Forest RegressionĪs machine learning practitioners, we come across a wide array of machine learning algorithms that we may apply to build our model.
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