Vertaile menetelmiä

	Monikerki-kerrosperceptron (MLP)	Logistinen regressio	XGBoost
Tieteenala	Syväoppiminen	Tutkimuksen tilastomenetelmät	Koneoppiminen
Menetelmäperhe	Machine learning	Process / pipeline	Machine learning
Syntyvuosi	1986	1958	2016
Kehittäjä	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.	David Roxbee Cox	Chen, T. & Guestrin, C.
Tyyppi	Supervised feedforward neural network	Method	Ensemble (gradient-boosted decision trees)
Alkuperäislähde	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.
Rinnakkaisnimet	MLP, feedforward neural network, fully connected neural network, vanilla neural network	logit model, binomial logistic regression, LR	XGBoost, extreme gradient boosting, scalable tree boosting
Liittyvät	4	3	5
Tiivistelmä	A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of 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.