Akaike Information Criterion (AIC)
Akaike Information Criterion er et informations-teoretisk mål for modelvalg, der balancerer god tilpasning mod modelkompleksitet. Introduceret af Hirotugu Akaike i 1974, estimerer AIC den relative kvalitet af modeller for et givet datasæt ved at straffe yderligere parametre for at forhindre overfitting.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. DOI: 10.1109/TAC.1974.1100705 ↗
- Burnham, K. P., & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.). New York: Springer. DOI: 10.2307/3802723 ↗
- Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79-86. DOI: 10.1214/aoms/1177729694 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Akaike Information Criterion. ScholarGate. https://scholargate.app/da/model-evaluation/akaike-information-criterion
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Justeret R-kvadrat (R²_adj)Modelevaluering↔ compare
- Bayesiansk informationskriterium (BIC)Modelevaluering↔ compare
- Middelfejlskvadrat (MSE)Modelevaluering↔ compare
- Determinationskoefficienten (R²)Modelevaluering↔ compare
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