Gradient Boosting is a machine learning technique that combines many simple prediction models, usually decision trees, to create a stronger and more accurate model. Instead of building all the trees independently, the method adds them one at a time, with each new tree focusing on correcting errors made by the previous ones.
Gradient Boosting is widely used for classification and prediction tasks involving structured data. It can be applied to areas such as fraud detection, customer analysis, forecasting, risk assessment, and recommendation systems. The technique is effective because each additional model improves the overall prediction by addressing weaknesses in the existing model. Popular implementations include XGBoost, LightGBM, and CatBoost, which provide optimized versions designed to handle large and complex datasets efficiently.
