Random Forest

Random Forest is a popular machine learning algorithm that is used for both classification and regression tasks. It is an ensemble method that combines multiple decision trees to make a prediction.
Random Forest works by creating multiple decision trees, each trained on a randomly selected subset of the training data and a randomly selected subset of the features. Each tree in the forest is trained independently and predicts the outcome based on its own set of rules.
To make a prediction, each decision tree in the forest independently generates a prediction, and the final prediction is made by aggregating the predictions of all the trees. For classification problems, the most common prediction made by the trees is selected, and for regression problems, the average of all the predictions made by the trees is taken.
The benefits of Random Forest include its ability to handle large datasets, high dimensional feature spaces, and noisy data. It is also less prone to overfitting compared to individual decision trees. Additionally, Random Forest can provide insights into the importance of different features in making predictions.
However, Random Forest can be computationally expensive and difficult to interpret compared to other machine learning algorithms. It also requires careful tuning of hyperparameters such as the number of trees and the depth of each tree to achieve optimal performance.
Category:AI