Comparer des méthodes

	Arbre de décision	Analyse en composantes principales
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1984	2002
Auteur d'origine	Breiman, Friedman, Olshen & Stone	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Recursive partitioning (if-then rules)	Unsupervised dimensionality reduction
Source fondatrice	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Apparentées	5	3
Résumé	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.