GBDT (Gradient Boosting Decision Trees)

Gradient Boosting Decision Trees (GBDT) are a machine learning method that builds a powerful predictive model by combining many small decision trees. It works iteratively: each new tree focuses on correcting errors made by the previous ones. By gradually improving accuracy through this process, GBDT can effectively handle complex patterns in data. It's widely used for tasks like ranking, classification, and regression because it balances interpretability with high performance. Essentially, GBDT learns step-by-step, refining its predictions to produce a precise overall model.