विधियों की तुलना करें

	आइसोलेशन फ़ॉरेस्ट	निर्णय वृक्ष	प्रिंसिपल कंपोनेंट एनालिसिस	रैंडम फ़ॉरेस्ट
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	2008	1984	2002	2001
प्रवर्तक	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breiman, Friedman, Olshen & Stone	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Breiman, L.
प्रकार	Unsupervised ensemble (random partitioning trees)	Recursive partitioning (if-then rules)	Unsupervised dimensionality reduction	Ensemble (bagging of decision trees)
मौलिक स्रोत	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
उपनाम	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	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
संबंधित	5	5	3	4
सारांश	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.	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.