Comparer des méthodes

	Analyse discriminante linéaire (ADL)	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Latent structure	Machine learning
Année d'origine	1936	2001
Auteur d'origine	Fisher, R. A.	Breiman, L.
Type	Supervised dimensionality reduction and linear classifier	Ensemble (bagging of decision trees)
Source fondatrice	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	4	4
Résumé	Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.	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.