Gradient boosting is a greedy algorithm and can overfit a training dataset quickly. It can benefit from regularization methods that penalize various parts of the algorithm and generally improve the performance of the algorithm by reducing overfitting.

How does gradient boosting algorithm work?

Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. ... If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero.

What is XGBoost algorithm in machine learning?

XGBoost is an algorithm that has recently been dominating applied machine learning and Kaggle competitions for structured or tabular data. XGBoost is an implementation of gradient boosted decision trees designed for speed and performance. ... Why XGBoost must be a part of your machine learning toolkit.