From Black Box to Shining Spotlight: Using Random Forest Prediction Intervals to Illuminate the Impact of Assumptions in Linear Regression.
We introduce a pair of Shiny web applications that allow users to visualize random forest prediction intervals alongside those produced by linear regression models. The apps are designed to help undergraduate students deepen their understanding of the role that assumptions play in statistical modeli...
| Publicado en: | American Statistician Vol. 76; no. 4; pp. 414 - 430 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Nov2022
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| 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=160099143&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 160099143 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: Nov2022 vid: 76 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 160099143 10.1080/00031305.2022.2107568 ppf: 414 ppct: 16 formats: tig: atl: From Black Box to Shining Spotlight: Using Random Forest Prediction Intervals to Illuminate the Impact of Assumptions in Linear Regression. aug: au: Sage, Andrew J. Liu, Yang Sato, Joe affil: Department of Mathematics, Statistics, and Computer Science, Lawrence University, Appleton, WI su: Forecasting Random forest algorithms Regression analysis Statistical models Educational objectives Web-based user interfaces sug: subj: Forecasting Random forest algorithms Regression analysis Statistical models Educational objectives Web-based user interfaces keyword: Interactive visualization Machine learning Statistics education Interactive visualization Machine learning Statistics education ab: We introduce a pair of Shiny web applications that allow users to visualize random forest prediction intervals alongside those produced by linear regression models. The apps are designed to help undergraduate students deepen their understanding of the role that assumptions play in statistical modeling by comparing and contrasting intervals produced by regression models with those produced by more flexible algorithmic techniques. We describe the mechanics of each approach, illustrate the features of the apps, provide examples highlighting the insights students can gain through their use, and discuss our experience implementing them in an undergraduate class. We argue that, contrary to their reputation as a black box, random forests can be used as a spotlight, for educational purposes, illuminating the role of assumptions in regression models and their impact on the shape, width, and coverage rates of prediction intervals. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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