Random Forest Algorithm

 Random Forest Algorithm

The Random Forest algorithm is an ensemble model that uses "bagging" as the ensemble method and the individual model as the decision tree. It is a learning method that works by constructing multiple decision trees and the final decision is made based on the majority of trees and selected by random forest.

Random forest comes under supervised learning and can be used for classification as well as regression problems. But mostly, it is used for classification problems.
A decision tree algorithm is a tree-shaped diagram that is used to determine a course of action. In the decision tree, each branch of the tree represents a possible decision, event, or response.

Random forest algorithm

Why we use a Random Forest Algorithm?

One of the main benefits of using Random Forest is the algorithm among the many benefits that it reduces the risk of overfitting as well as the required training time. Additionally, it provides a high degree of accuracy. The random forest algorithm runs efficiently in large datasets and also makes highly accurate predictions by estimating missing data.

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