Comparer des méthodes

	Analyse Discriminante Linéaire (ADL	Forêt Aléatoire
Domaine	Statistique	Apprentissage automatique
Famille	Hypothesis test	Machine learning
Année d'origine	1936	2001
Auteur d'origine	Ronald A. Fisher	Breiman, L.
Type	Parametric linear classifier / dimensionality reduction	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 LDA, Fisher's linear discriminant, discriminant function analysis	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	7	4
Résumé	Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA.	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.