# LightGBM LightGBM is one of the most popular machine learning algorithms for building predictive models for both classification and regression tasks. It belongs to the same gradient boosting family as XGBoost, while achieving faster training and better memory efficiency. For example, techniques such as the "Leaf-wise" strategy that prioritizes splitting only the leaf that most improves prediction accuracy, GOSS that skips data already being predicted well, and EFB that bundles features that don't take values at the same time, enable efficient model building even on large-scale data. For more details on how to use it, please refer to the following articles. * [Introduction to LightGBM](https://exploratory.io/note/exploratory/Introduction-to-LightGBM-eAK7zbZ9) * [LightGBMの紹介](https://ja.exploratory.io/note/exploratory/LightGBM-FsO3pPj3) --- # 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://docs.exploratory.io/analytics/lightgbm.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.