The LightGBM framework supports different algorithms including GBT, GBDT, GBRT, GBM, MART[6][7] and RF.

[8] LightGBM has many of XGBoost's advantages, including sparse optimization, parallel training, multiple loss functions, regularization, bagging, and early stopping.

[10] Besides, LightGBM does not use the widely used sorted-based decision tree learning algorithm, which searches the best split point on sorted feature values,[11] as XGBoost or other implementations do.

Instead, LightGBM implements a highly optimized histogram-based decision tree learning algorithm, which yields great advantages on both efficiency and memory consumption.

[16] Gradient-based one-side sampling (GOSS) is a method that leverages the fact that there is no native weight for data instance in GBDT.