Random Forest, Explained: A Visual Guide with Code Examples

Random Forests combine multiple decision trees to improve predictions in machine learning tasks by using ensemble learning. Each tree is trained on a random sample of the data and considers a random subset of features when making splits, enhancing robustness and accuracy. This guide explains the mechanics, training processes, key parameters, pros, and cons of Random Forests, illustrated with Python code examples using the classic golf dataset for classification tasks.