Salīdzināt metodes

	Isolation Forest	Nenoteiktības kvantifikācija
Nozare	Mašīnmācīšanās	Simulācija
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2008	Seminal modern form: 2002
Autors	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Norbert Wiener (polynomial chaos, 1938); extended to Wiener–Askey scheme by Xiu & Karniadakis (2002)
Tips	Unsupervised ensemble (random partitioning trees)	Computational uncertainty analysis framework
Pirmavots	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Xiu, D. & Karniadakis, G.E. (2002). The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations. SIAM Journal on Scientific Computing, 24(2), 619–644.
Citi nosaukumi	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	UQ, polynomial chaos expansion, PCE, Kriging surrogate
Saistītās	5	9
Kopsavilkums	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.	Uncertainty Quantification (UQ) is a computational framework for systematically measuring how uncertainty in the inputs of a model propagates into uncertainty in its outputs. Building on Wiener's polynomial chaos theory (1938) and formalised for general stochastic problems by Xiu and Karniadakis (2002), UQ uses two primary strategies: Polynomial Chaos Expansion (PCE), which represents the model output as a series of orthogonal polynomials matched to the input distributions, and Kriging (Gaussian process) surrogates, which replace an expensive simulation with a fast statistical approximation fitted to a small set of carefully chosen runs.