Gradient Boosting is a powerful machine learning technique that builds a strong predictive model in an additive, sequential manner. It works by iteratively training new models (typically decision trees) to predict the residuals or errors of the previous models. By combining these weak learners, the overall model gradually improves its accuracy, effectively performing a gradient descent optimization in the function space.
The core idea of gradient boosting was introduced by Jerome H. Friedman in his 2001 paper 'Greedy Function Approximation: A Gradient Boosting Machine'. This work laid the theoretical foundation for many modern boosting algorithms.
Gradient Boosting is widely regarded as one of the most effective algorithms for structured or tabular data, and it has been the winning solution in many Kaggle competitions. Its popularity has led to the development of highly optimized and efficient implementations, such as XGBoost, LightGBM, and CatBoost, which are now standard tools for data scientists.