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Simplifying Random Forests' Probabilistic Forecasts.

Since their introduction by Breiman, Random Forests (RFs) have proven to be useful for both classification and regression tasks. The RF prediction of a previously unseen observation can be represented as a weighted sum of all training sample observations. This nearest-neighbor-type representation is...

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Publicado en:American Statistician Vol. 80; no. 1; pp. 135 - 146
Autores principales: Koster, Nils, Krüger, Fabian
Formato: Artículo
Publicado: Taylor & Francis Ltd Feb2026
Materias:
Forecasting
Random forest algorithms
Machine learning
Distribution (Probability theory)
Statistical models
K-nearest neighbor classification
Parsimonious models
Acceso en línea:Ver este registro en EBSCOhost