Gradient Boosting Explained

Gradient Boosting builds models sequentially, with each new model trying to reduce the errors left by the combined previous models.

Instead of just focusing on misclassified points like AdaBoost, it specifically targets the residual errors, the gap between actual and predicted values, and trains the next model to minimize that gap.

• Each new tree learns from the mistakes of all previous trees combined, not just the last one.
• A learning rate controls how much each new model contributes, preventing the overall model from overcorrecting.
• It generally produces highly accurate results but can overfit if not tuned carefully.

Examples

• Predicting customer lifetime value by continuously refining error patterns from earlier predictions.
• Ranking search engine results, where gradient boosting refines relevance scores step by step.