विधियों की तुलना करें

	लाइटजीबीएम	XGBoost
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017	2016
प्रवर्तक	Ke, G. et al. (Microsoft)	Chen, T. & Guestrin, C.
प्रकार	Gradient boosting decision tree ensemble	Ensemble (gradient-boosted decision trees)
मौलिक स्रोत	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
उपनाम	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	XGBoost, extreme gradient boosting, scalable tree boosting
संबंधित	5	5
सारांश	LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.