পদ্ধতির তুলনা করুন

	এনসেম্বল গ্রেডিয়েন্ট বুস্টিং	লাইটজিবিএম
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001	2017
প্রবর্তক	Friedman, J. H.	Ke, G. et al. (Microsoft)
ধরন	Ensemble (sequential boosting of decision trees)	Gradient boosting decision tree ensemble
মৌলিক উৎস	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	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.
অপর নাম	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
সম্পর্কিত	6	5
সারসংক্ষেপ	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	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.