LightGBM is an open source distributed gradient boosting framework developed by Microsoft. It is used for various machine learning tasks including classification, regression, and ranking. The framework is designed to provide high performance, scalability, and speed on large datasets. The objective function is optimized to reduce the time complexity of the fitting process, and it also takes advantage of GPUs to achieve better performance.
LightGBM is well-suited for creating large-scale predictive applications and is popular in many industries such as finance, healthcare, and retail. It has also been used in research and scientific applications.
LightGBM was originally created by Microsoft Research Asia and open sourced on GitHub in 2017. The main advantages of LightGBM are its high performance, efficiency, and scalability. It provides a fast and accurate algorithm for large datasets, and it supports both categorical and numerical features.
LightGBM works well in many scenarios and does not require extensive hyperparameter tuning like many other boosting algorithms. It offers high accuracy and fast training time. It also provides efficient parallel compute functions for multiple GPUs and CPUs.
LightGBM can be used along with other platforms such as XGBoost, CatBoost, and Microsoft’s Cognitive Toolkit. LightGBM can be integrated with Python, R, and Java. It also has an API for C++, so it can be used for producing C++ based models.