Random Forest

Random Forest is an ensemble learning algorithm that combines multiple decision trees to achieve higher predictive accuracy. It supports both classification and regression tasks and is widely used in practice for its high generalization performance and stability. Key characteristics of Random Forest: • Each decision tree trains independently, and final predictions are made by majority vote (classification) or averaging (regression) • Random selection of features and samples during training helps suppress overfitting • Feature importance can be easily visualized, providing good interpretability • High resistance to outliers and noise, enabling stable predictions The Random Forest algorithm is based on a technique called "bagging" (bootstrap aggregating), in which multiple models are built using different subsets of the training data with the same algorithm (decision trees), allowing the models to compensate for each other's weaknesses. Representative use cases: • Customer churn prediction and purchase behavior analysis • Disease risk diagnosis support (medical data classification) • Fraud detection • Anomaly detection and classification in manufacturing and quality control • Risk assessment and credit scoring for financial products Despite the rise of deep learning, Random Forest remains a trusted machine learning model in practical applications — particularly strong on structured tabular data and relatively low in computational cost.