Random Forest Classifier Trend 2026

A random forest classifier combines multiple decision trees to improve prediction accuracy and reduce overfitting. It handles classification tasks by aggregating votes from many trees trained on random data subsets. Data scientists, machine learning engineers, and analysts use it for tasks like fraud detection, medical diagnosis, and customer segmentation.