Compara mètodes

	Perceptró Multicapa (MLP)	XGBoost
Camp	Aprenentatge profund	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	1986	2016
Autor original	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.	Chen, T. & Guestrin, C.
Tipus	Supervised feedforward neural network	Ensemble (gradient-boosted decision trees)
Font seminal	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.
Àlies	MLP, feedforward neural network, fully connected neural network, vanilla neural network	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionats	4	5
Resum	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.	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.