# Gradient Boosting ![Source: Hands-On Machine Learning with R](https://2327526407-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LvBP1svpACTB1R1x_U4%2F-Lw6zezdliKWkGknCJ6R%2F-Lw70EB_T-Y3OCO-L_4o%2Fimage.png?alt=media\&token=a3edaf4a-d3d2-4c84-9a10-3d870c21d641) 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](https://machine-learning.paperspace.com/accuracy-and-loss#loss) that is optimized with [gradient descent](https://machine-learning.paperspace.com/wiki/gradient-descent). Decision trees are typically the weak learners in gradient boosting and consequently, the technique is sometimes referred to as *gradient tree boosting*. ![](https://2327526407-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-LvBP1svpACTB1R1x_U4%2F-Lw6zezdliKWkGknCJ6R%2F-Lw70ZzNl5AgerhBsp-v%2Fimage.png?alt=media\&token=5df440f0-a102-454e-9d43-e07549dd5334) [XGBoost](https://machine-learning.paperspace.com/comparison-of-ai-frameworks#ml-frameworks) is a very popular gradient boosting framework that is fast, uses some clever tricks to obtain more accurate results, and is easy to parallelize. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://machine-learning.paperspace.com/wiki/gradient-boosting.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.