Random Forest is a powerful machine learning algorithm that belongs to the family of ensemble methods. It is primarily used for classification and regression tasks and is known for its accuracy, versatility, and resistance to overfitting. The name “Random Forest” reflects the way it operates: it builds a large number of decision trees and combines their outputs to make a final prediction.
At its core, a decision tree makes decisions by learning rules from data in a step-by-step, tree-like structure. Each node in a tree represents a feature (or attribute) from the dataset, and branches represent decision outcomes. However, a single decision tree can be unstable and sensitive to noise in the data, often leading to overfitting, especially on small or complex datasets.
Random Forest overcomes this limitation by creating a collection—or “forest”—of decision trees. These trees are built independently from each other, and each one is trained on a different random subset of the training data. This technique is called “bagging,” or bootstrap aggregating, where each subset is created by sampling with replacement. As a result, each tree sees a slightly different version of the data and may capture different patterns or structures.
Moreover, when building each tree, Random Forest adds another layer of randomness by selecting a random subset of features at each split instead of considering all available features. This ensures that the trees are not too similar and that the ensemble as a whole captures a diverse range of decision rules.
When it’s time to make a prediction, Random Forest uses a simple but effective approach: in classification tasks, it takes a majority vote among all the trees’ outputs; in regression tasks, it averages the outputs. This aggregation smooths out the variance of individual trees, improving generalization and performance on unseen data.
Because of this design, Random Forest is well-suited to datasets with missing values, high dimensionality, or noisy features. It works effectively without requiring much parameter tuning and offers feature importance metrics, which can help identify which input variables are most influential in making predictions.
Random Forest combines the simplicity of decision trees with the robustness of ensemble learning, making it one of the most widely used and reliable algorithms in the machine learning toolbox.