Comparer des méthodes

	Perceptron multicouche explicable	Forêt Aléatoire
Domaine	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2010s–present	2001
Auteur d'origine	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); broader XAI community	Breiman, L.
Type	Supervised feedforward neural network with interpretability layer	Ensemble (bagging of decision trees)
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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	XMLP, Interpretable MLP, Explainable feedforward neural network, Transparent MLP	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.