Comparer des méthodes

	Bagging (Bootstrap Aggregating)	Arbre de décision	Gradient Boosting	Isolation Forest
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning	Machine learning
Année d'origine	1996	1984	2001	2008
Auteur d'origine	Breiman, L.	Breiman, Friedman, Olshen & Stone	Friedman, J. H.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Type	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Recursive partitioning (if-then rules)	Ensemble (sequential boosting of decision trees)	Unsupervised ensemble (random partitioning trees)
Source fondatrice	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Apparentées	5	5	5	5
Résumé	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.