Random forests are a machine learning method utilizing multiple decision trees for predictions, improving accuracy and reducing overfitting. They differ from single decision trees by combining outputs from numerous trees trained on varied data samples. Random forests apply to diverse problems across sectors, such as healthcare for patient classification and finance for loan predictions. Advantages include accuracy, versatility, and feature importance estimation, while drawbacks involve complexity, higher computational cost, and slower predictions compared to simpler models.
Random Forests in Machine Learning: What They Are and How They Work
