A Practical Guide to Random Forests in Machine Learning

Random Forest combines multiple decision trees to create a powerful machine learning algorithm for classification and regression. It uses random sampling of data and features to build diverse trees, then aggregates their predictions. This approach reduces overfitting and improves accuracy compared to single decision trees. Random Forest is widely used in various fields due to its robustness and ability to handle complex datasets.