LightGBM
LightGBM (Light Gradient Boosting Machine) is another powerful gradient boosting framework designed for efficiency and performance. Developed by Microsoft, LightGBM is optimized for speed and memory usage, making it an excellent choice for large-scale machine learning applications.
Unlike traditional tree-based models, LightGBM uses a leaf-wise growth strategy, allowing it to handle large datasets and high-dimensional features with ease. This approach improves accuracy while reducing computation time, making it particularly useful for ranking and recommendation systems.
LightGBM is widely used in competitive data science, including Kaggle competitions, due to its ability to handle missing values and categorical data efficiently. It is commonly applied in predictive analytics, customer behavior modeling, and fraud detection systems.