A Random Forest is an ensemble learning method used for both classification and regression tasks. It operates by constructing a multitude of decision trees during training and outputting the mode (for classification) or mean prediction (for regression) of the individual trees as the final prediction.

Here's how a Random Forest works:

Bootstrap Sampling: The Random Forest algorithm begins by randomly selecting subsets of the training data with replacement (bootstrap sampling). Each subset, known as a bootstrap sample, is used to train a separate decision tree.

Feature Randomization: At each node of the decision tree, a random subset of features is selected as candidates for splitting. This helps to introduce diversity among the trees and prevent them from becoming overly correlated.

Decision Tree Construction: Each decision tree in the Random Forest is trained independently using the bootstrap sample and a subset of features. The trees are typically grown to their maximum depth or until a minimum number of samples per leaf node is reached.

Voting or Averaging: For classification tasks, the predictions of all the individual decision trees are combined using a majority voting scheme, where the class that receives the most votes is selected as the final prediction. For regression tasks, the predictions are averaged across all the trees to produce the final prediction.

Random Forests offer several advantages, including:

• Robustness to Overfitting: By averaging the predictions of multiple trees, Random Forests reduce the risk of overfitting and improve generalization performance.