Gradient Boosting

Gradient boosting (derived from the term gradient boosting machines) is a popular supervised machine learning technique for regression and classification problems that aggregates an ensemble of weak individual models to obtain a more accurate final model.

Gradient boosting is a unique ensemble method since it involves identifying the shortcomings of weak models and incrementally or sequentially building a final ensemble model using a loss function that is optimized with gradient descent. Decision trees are typically the weak learners in gradient boosting and consequently, the technique is sometimes referred to as gradient tree boosting.

XGBoost is a very popular gradient boosting framework that is fast, uses some clever tricks to obtain more accurate results, and is easy to parallelize.