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	Isolation Forest	Analiza Głównych Składowych	Random Forest
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2008	2002	2001
Twórca	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Breiman, L.
Typ	Unsupervised ensemble (random partitioning trees)	Unsupervised dimensionality reduction	Ensemble (bagging of decision trees)
Źródło pierwotne	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Inne nazwy	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Pokrewne	5	3	4
Podsumowanie	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.	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.	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.