Random Forest is a powerful machine learning algorithm that belongs to the ensemble learning family. Instead of relying on a single decision tree, Random Forest creates a forest of multiple decision trees and combines their predictions to produce a final result. This approach makes the model more accurate and less prone to overfitting compared to individual decision trees.
The first key component of Random Forest is Bootstrap Aggregating, or Bagging. This process starts with the original training dataset and creates multiple bootstrap samples by randomly selecting data points with replacement. Each bootstrap sample has the same size as the original dataset, but some data points may appear multiple times while others may not appear at all. Each of these bootstrap samples is then used to train a separate decision tree, creating diversity among the trees in the forest.
The second key component is the Random Subspace Method, which introduces feature randomness. When building each decision tree, at every node split, the algorithm doesn't consider all available features. Instead, it randomly selects a subset of features to evaluate for the best split. This random feature selection adds another layer of diversity to the forest, ensuring that different trees focus on different aspects of the data and helping to decorrelate the individual trees.
The final step is making predictions with the trained Random Forest. When a new data point arrives, it's fed to all trees in the forest. For classification problems, each tree votes for a class, and the class with the majority vote becomes the final prediction. For regression problems, each tree outputs a numerical value, and the final prediction is the average of all these values. This ensemble approach typically produces more accurate and stable predictions than any individual tree.
Random Forest offers several key advantages over single decision trees. It significantly reduces overfitting by averaging multiple trees, handles missing values naturally, and provides feature importance rankings. The algorithm is robust to outliers, works well with large datasets, and doesn't require feature scaling. Compared to a single decision tree that might achieve 75% accuracy, a Random Forest typically achieves much higher accuracy, often above 90%, making it one of the most reliable machine learning algorithms for both classification and regression tasks.