Võrdle meetodeid

	Stohhastiline gradiendilange (SGD)	Logistiline regressioon	XGBoost
Valdkond	Masinõpe	Uurimisstatistika	Masinõpe
Perekond	Machine learning	Process / pipeline	Machine learning
Tekkeaasta	1951	1958	2016
Looja	Robbins, H. & Monro, S.	David Roxbee Cox	Chen, T. & Guestrin, C.
Tüüp	First-order iterative optimization algorithm	Method	Ensemble (gradient-boosted decision trees)
Algallikas	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Rööpnimetused	SGD, online gradient descent, incremental gradient descent, mini-batch gradient descent	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
Seotud	3	3	5
Kokkuvõte	Stochastic Gradient Descent (SGD) is a first-order iterative optimization algorithm, rooted in the stochastic approximation framework introduced by Robbins and Monro in 1951, that minimizes an objective function by updating model parameters using the gradient computed on a single randomly selected training example (or a small mini-batch) at each step. It is the core optimization engine behind modern machine learning and deep learning, enabling the training of models on datasets too large to fit in memory.	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.	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.