পদ্ধতির তুলনা করুন

	আইসোলেশন ফরেস্ট	প্রধান উপাদান বিশ্লেষণ
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2008	2002
প্রবর্তক	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
ধরন	Unsupervised ensemble (random partitioning trees)	Unsupervised dimensionality reduction
মৌলিক উৎস	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.
অপর নাম	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
সম্পর্কিত	5	3
সারসংক্ষেপ	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.