পদ্ধতির তুলনা করুন

	বেয়েশিয়ান এক্সট্রিম গ্রেডিয়েন্ট বুস্টিং (Bayesian XGBoost)	লাইটজিবিএম
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2012–2016	2017
প্রবর্তক	Chen, T. & Guestrin, C. (XGBoost); Snoek, J. et al. (Bayesian Optimization)	Ke, G. et al. (Microsoft)
ধরন	Ensemble (gradient boosted trees with Bayesian hyperparameter search)	Gradient boosting decision tree ensemble
মৌলিক উৎস	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.	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	Bayesian XGBoost, XGBoost with Bayesian Optimization, BayesOpt-XGBoost, Bayes-tuned XGBoost	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
সম্পর্কিত	4	5
সারসংক্ষেপ	Bayesian XGBoost combines the predictive power of Extreme Gradient Boosting with Bayesian optimization for hyperparameter tuning. Instead of grid or random search, a probabilistic surrogate model guides the search for optimal learning rate, tree depth, and regularization parameters, achieving near-peak performance with far fewer evaluations than exhaustive search approaches.	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.