Compara mètodes

	LightGBM	Regressió Logística
Camp	Aprenentatge automàtic	Estadística per a la recerca
Família	Machine learning	Process / pipeline
Any d'origen	2017	1958
Autor original	Ke, G. et al. (Microsoft)	David Roxbee Cox
Tipus	Gradient boosting decision tree ensemble	Method
Font seminal	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Àlies	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	logit model, binomial logistic regression, LR
Relacionats	5	3
Resum	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.