Random Forest is an ensemble learning technique, which means it combines multiple individual models to make more robust and accurate predictions. This ensemble approach leverages the wisdom of the crowd by aggregating the predictions of multiple models, reducing the risk of overfitting, and improving overall performance.
Bagging: It employs a technique called bagging to create diverse training sets for each decision tree. Bagging involves random sampling with replacement from the original training dataset to create multiple subsets, often referred to as “bootstrap samples.” Each decision tree is trained on one of these bootstrap samples. This diversity helps prevent individual decision trees from overfitting to the training data.
Random Feature Selection: Another key feature of Random Forest is the random selection of features at each split node when constructing decision trees. Instead of considering all available features for the best split at each node, Random Forest randomly selects a subset of features to consider. This random feature selection reduces the correlation between trees and improves the model’s generalization ability.