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	बायेसियन LightGBM	लाइटजीबीएम
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2017 (LightGBM); 2012 (Bayesian optimization)	2017
प्रवर्तक	Ke et al. (LightGBM); Snoek et al. (Bayesian optimization)	Ke, G. et al. (Microsoft)
प्रकार	Gradient boosting with Bayesian hyperparameter search	Gradient boosting decision tree ensemble
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. In Advances in Neural Information Processing Systems, 30, 3146–3154.	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.
उपनाम	Bayesian-tuned LightGBM, LightGBM + Bayesian optimization, BayesOpt LightGBM, LightGBM with BayesOpt	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
संबंधित	5	5
सारांश	Bayesian LightGBM combines LightGBM — a highly efficient histogram-based gradient boosting framework — with Bayesian hyperparameter optimization. Instead of exhaustive grid search or random search, a probabilistic surrogate model guides the search for optimal hyperparameters, dramatically reducing the number of costly model evaluations needed to reach strong predictive performance.	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.