Random Forests
Random Forests is an ensemble machine learning method that operates by constructing a multitude of decision trees during training and outputting the mode of the classes (classification) or mean prediction (regression) of the individual trees. It combines bagging (bootstrap aggregating) with random feature selection to reduce overfitting and improve accuracy compared to single decision trees. The method is known for its robustness, ability to handle high-dimensional data, and built-in feature importance estimation.
Developers should learn Random Forests when working on supervised learning tasks like classification or regression, especially with tabular data, as it often provides strong out-of-the-box performance with minimal hyperparameter tuning. It is particularly useful in domains like finance, healthcare, and marketing for tasks such as fraud detection, disease prediction, or customer segmentation, where interpretability and handling of missing values are important. Its ensemble nature makes it less prone to overfitting than individual models like decision trees.