Comparer des méthodes

	Perceptron multicouche explicable	Perceptron multicouche (MLP)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2010s–present	1986
Auteur d'origine	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); broader XAI community	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.
Type	Supervised feedforward neural network with interpretability layer	Supervised feedforward neural network
Source fondatrice	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
Alias	XMLP, Interpretable MLP, Explainable feedforward neural network, Transparent MLP	MLP, feedforward neural network, fully connected neural network, vanilla neural network
Apparentées	4	4
Résumé	An Explainable Multilayer Perceptron (XMLP) is a standard feedforward neural network trained with backpropagation, augmented with post-hoc interpretability techniques — such as SHAP values, LIME, or integrated gradients — that attribute each prediction to individual input features. The combination retains the MLP's approximation power while satisfying transparency requirements common in regulated or high-stakes domains.	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.