Member-only story
Introduction
In today’s data-driven world, making accurate predictions is critical for business success. One algorithm stands out for its remarkable ability to handle complex data while remaining robust and interpretable: Random Forest. Whether you’re predicting customer behavior, diagnosing diseases, detecting fraud, or optimizing trading strategies, Random Forest has proven itself as a reliable tool across industries.
Despite its powerful name and sophisticated appearance, Random Forest is built on surprisingly simple and elegant principles. It leverages the wisdom of many to outperform any single expert — much like asking multiple knowledgeable people for advice rather than relying on just one specialist.
This comprehensive guide explores everything you need to know about Random Forest: what it is, how it works, its various types, real-world applications.
What is Random Forest?
Random Forest is an ensemble machine learning algorithm that combines predictions from multiple decision trees to create a more accurate and robust model. It’s a supervised learning algorithm capable of solving both classification (predicting categories) and regression (predicting continuous values) problems.
