LightGBM is an open-source gradient boosting framework developed by Microsoft. It is designed for efficient training of machine learning models, particularly for large datasets. By using a histogram-based approach, LightGBM speeds up the training process and reduces memory usage compared to traditional gradient boosting methods. One of the key features of LightGBM is its ability to handle categorical features directly, which simplifies data preprocessing. It also supports parallel and distributed learning, making it suitable for both small and large-scale applications. Overall, LightGBM is popular among data scientists for its performance and scalability.