Porovnat metody

	Multi-layer Perceptron (MLP)	Logistická regrese	XGBoost
Obor	Strojové učení	Statistika ve výzkumu	Strojové učení
Rodina	Machine learning	Process / pipeline	Machine learning
Rok vzniku	1986	1958	2016
Tvůrce	Rumelhart, D. E., Hinton, G. E., & Williams, R. J.	David Roxbee Cox	Chen, T. & Guestrin, C.
Typ	Feedforward neural network (supervised learning)	Method	Ensemble (gradient-boosted decision trees)
Původní zdroj	Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	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.
Další názvy	MLP, feedforward neural network, fully connected neural network, artificial neural network	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
Příbuzné	4	3	5
Shrnutí	The Multi-layer Perceptron (MLP) is a feedforward neural network architecture trained by backpropagation, formalised by Rumelhart, Hinton, and Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons with nonlinear activation functions, and an output layer, the MLP can approximate any continuous function to arbitrary accuracy and serves as the conceptual bridge between classical machine learning and modern deep learning.	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.