Sammenlign metoder

	Faktoranalyse	Hovedkomponentanalyse	Random Forest
Fagfelt	Forskningsstatistikk	Maskinlæring	Maskinlæring
Familie	Process / pipeline	Machine learning	Machine learning
Opprinnelsesår	1931	2002	2001
Opphavsperson	Louis Leon Thurstone	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Breiman, L.
Type	Method	Unsupervised dimensionality reduction	Ensemble (bagging of decision trees)
Opprinnelig kilde	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	EFA, CFA, latent variable modeling	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relaterte	3	3	4
Sammendrag	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	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.