Enhancing Prediction Accuracy Through Random Forest in Classification and Regression

Random Forest is a well-known type of ensemble learning, which combines a number of decision trees to improve the prediction ability and reduce the risk of overfitting. This paper aims at discussing in detail Random Forest with emphasis on basic concepts such as decision trees and ensemble approaches including bagging. Here, we discuss the factors like training process, prediction procedure, and feature significance measurement. The paper covers a range of applications including classification, regression, anomaly detection and feature selection. We also describe the strengths of the algorithm, overfitting, and the ability to perform on different tasks. But issues like high computational costs, issues with model interpretability, and biases in the imbalanced datasets are also solved, or at least partially dealt with. Emerging trends and future developments are discussed, and they focus on advancements in accuracy, realtime management, and the interaction with deep learning. This survey clearly shows how Random Forest is still useful and can be further improved for it to be effective in the contemporary machine learning techniques.