Random forest is a popular supervised machine learning algorithm—used for both classification and regression problems. It is based on the concept of ensemble learning, which enables users to combine multiple classifiers to solve a complex problem and to also improve the performance of the model.
The random forest algorithm relies on multiple decision trees and accepts the results of the predictions from each tree. Based on the majority votes of predictions, it determines the final result.
The following is an example of what a random forest classifier in general looks like:
The classifier contains training datasets; each training dataset contains different values. Multiple decision tree models are created with the help of these datasets. Based on the output of these models, a vote is carried out to find the result with the highest frequency. A test set is evaluated based on these outputs to get the final predicted results.