Random Forest Classifier(How Does It Work?)

One of the most popular supervised algorithms used in Machine Learning is the Random Forest Classifier. A Random Forest Classifier is an ensemble learning method that builds multiple decision trees and combines them to create a single model that can be used for both classification and regression tasks. This article will explain how a Random Forest Classifier works and how it can be used to solve various problems in machine learning.

How Does Random Forest ClassifierWorks?

The classifier works by building multiple decision trees which are then combined to create a single unified model. Each decision tree takes into account different factors and is able to make predictions based on these factors. By combining the predictions of all of the decision trees, a more accurate and reliable result can be achieved.

The Random Forest classifier works by building multiple decision trees, each of which is trained on a different subset of the training data. It works by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

As shown in the following figure, there are three decision trees, A, B, and C for the classification of the fruits. Tree A classifies the fruits into either apple, banana, or papaya. Tree B classifies into apple, papaya, or banana. Tree C classifies them into either apple, papaya, or banana. Among…