Comparer des méthodes

	Isolation Forest	Arbre de décision	Analyse en composantes principales
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2008	1984	2002
Auteur d'origine	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breiman, Friedman, Olshen & Stone	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Unsupervised ensemble (random partitioning trees)	Recursive partitioning (if-then rules)	Unsupervised dimensionality reduction
Source fondatrice	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	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	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Apparentées	5	5	3
Résumé	Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets.	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.