Porovnat metody

	Multi-layer Perceptron (MLP)	XGBoost
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	1986	2016
Tvůrce	Rumelhart, D. E., Hinton, G. E., & Williams, R. J.	Chen, T. & Guestrin, C.
Typ	Feedforward neural network (supervised learning)	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.	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	XGBoost, extreme gradient boosting, scalable tree boosting
Příbuzné	4	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.	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.