Random Forest is a powerful ensemble learning method that combines multiple decision trees to create more accurate and robust predictions. Unlike using a single decision tree, Random Forest builds many trees and combines their predictions through voting for classification or averaging for regression. This approach provides key advantages including improved accuracy, reduced overfitting, and robustness to noise and missing values.
Decision trees are the fundamental building blocks of Random Forest. They work by recursively splitting the data based on feature values to make predictions. For example, when deciding whether to play tennis, a decision tree might first check the weather condition, then examine humidity levels for sunny days. While decision trees are intuitive and easy to interpret, they have significant limitations including tendency to overfit, high variance, and sensitivity to small changes in the training data.
Bootstrap sampling is the first key component that creates diversity in Random Forest. Starting with an original dataset of N samples, we create multiple bootstrap samples, each also of size N, by sampling with replacement. This means some data points will appear multiple times in a bootstrap sample, while others may not appear at all. For example, in one bootstrap sample, we might select the red point twice and skip the purple point entirely. This random sampling process ensures that each decision tree in the forest is trained on a slightly different dataset, creating the diversity that makes Random Forest so powerful.
Feature randomness is the second key component that adds diversity to Random Forest. At each node split in every decision tree, instead of considering all available features, Random Forest randomly selects only a subset of features. Typically, this subset size is the square root of the total number of features. For example, if we have 16 features, only 4 would be considered at each split. This random feature selection serves multiple purposes: it reduces correlation between trees, increases overall diversity, and prevents any single dominant feature from controlling all the splits. Different nodes may consider completely different feature subsets, leading to more varied and robust decision trees.
The ensemble voting mechanism is how Random Forest combines individual tree predictions to make final decisions. When given input data, each decision tree in the forest makes its own prediction independently. For classification problems, Random Forest uses majority voting where the class with the most votes becomes the final prediction. In this example, three trees predict 'Yes' and two predict 'No', so the final prediction is 'Yes'. For regression problems, the predictions are averaged instead. This aggregation process reduces variance and typically produces more accurate and robust predictions than any single tree could achieve alone.