Random Forest
Random Forest is a popular ensemble machine learning algorithm that operates by constructing multiple decision trees during training and outputting the mode of the classes (classification) or mean prediction (regression) of the individual trees. It combines the bagging technique with random feature selection to reduce overfitting and improve accuracy compared to single decision trees. The algorithm is known for its robustness, scalability, and ability to handle high-dimensional data with minimal tuning.
Developers should learn Random Forest when working on classification or regression problems where interpretability is less critical than predictive performance, such as in fraud detection, medical diagnosis, or customer churn prediction. It is particularly useful for datasets with many features, as it automatically performs feature importance analysis, and it handles missing values and outliers well without extensive preprocessing. Its ease of use and strong performance make it a go-to baseline model in many machine learning projects.