Сравнение на методи

	Многослоен персептрон (MLP)	Логистична регресия	Рекурентна невронна мрежа	XGBoost
Област	Машинно обучение	Статистика за изследвания	Дълбоко обучение	Машинно обучение
Семейство	Machine learning	Process / pipeline	Machine learning	Machine learning
Година на възникване	1986	1958	1986–1990	2016
Създател	Rumelhart, D. E., Hinton, G. E., & Williams, R. J.	David Roxbee Cox	Rumelhart, D. E.; Elman, J. L.	Chen, T. & Guestrin, C.
Тип	Feedforward neural network (supervised learning)	Method	Sequential neural network	Ensemble (gradient-boosted decision trees)
Основополагащ източник	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Други названия	MLP, feedforward neural network, fully connected neural network, artificial neural network	logit model, binomial logistic regression, LR	RNN, Elman network, Jordan network, simple recurrent network	XGBoost, extreme gradient boosting, scalable tree boosting
Свързани	4	3	3	5
Резюме	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.	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.	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.