Random Forest Prediction Intervals.
Random forests are among the most popular machine learning techniques for prediction problems. When using random forests to predict a quantitative response, an important but often overlooked challenge is the determination of prediction intervals that will contain an unobserved response value with a...
| Publicado en: | American Statistician Vol. 74; no. 4; pp. 392 - 407 |
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| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Nov2020
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=146709636&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 146709636 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Nov2020 vid: 74 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 146709636 10.1080/00031305.2019.1585288 ppf: 392 ppct: 15 formats: tig: atl: Random Forest Prediction Intervals. aug: au: Zhang, Haozhe Zimmerman, Joshua Nettleton, Dan Nordman, Daniel J. affil: Department of Statistics, Iowa State University, Ames, IA su: Forecasting Random forest algorithms Quantile regression Machine learning sug: subj: Forecasting Random forest algorithms Quantile regression Machine learning keyword: Conformal inference Coverage rate Interval width Out-of-bag prediction errors Quantile regression forests Conformal inference Coverage rate Interval width Out-of-bag prediction errors Quantile regression forests ab: Random forests are among the most popular machine learning techniques for prediction problems. When using random forests to predict a quantitative response, an important but often overlooked challenge is the determination of prediction intervals that will contain an unobserved response value with a specified probability. We propose new random forest prediction intervals that are based on the empirical distribution of out-of-bag prediction errors. These intervals can be obtained as a by-product of a single random forest. Under regularity conditions, we prove that the proposed intervals have asymptotically correct coverage rates. Simulation studies and analysis of 60 real datasets are used to compare the finite-sample properties of the proposed intervals with quantile regression forests and recently proposed split conformal intervals. The results indicate that intervals constructed with our proposed method tend to be narrower than those of competing methods while still maintaining marginal coverage rates approximately equal to nominal levels. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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