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random forest (rf) model  (Helmholtz Zentrum fur Infektionsforschung GmbH)

 
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    Helmholtz Zentrum fur Infektionsforschung GmbH random forest (rf) model
    Random Forest (Rf) Model, supplied by Helmholtz Zentrum fur Infektionsforschung GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/algorithmic+random+forest+model/random+forest++rf++model/pm37734577-10-8-44
    Average 90 stars, based on 1 article reviews
    random forest (rf) model - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Improved daily estimates of relative humidity at high resolution across Germany: A random forest approach.
    Article Snippet: Improved daily estimates of relative humidity at high resolution across Germany: A random forest approach Nikolaos Nikolaou a,b,*, Laurens M. Bouwer c, Marco Dallavalle a,b, Mahyar Valizadeh a, Massimo Stafoggia d, Annette Peters a,b, Kathrin Wolf a,1, Alexandra Schneider a,1 a Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany b Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, LMU Munich, Pettenkofer School of Public Health, Munich, Germany c Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany d Department of Epidemiology, Lazio Regional Health Service – ASL Roma 1, Rome, Italy A R T I C L E I N F O Handling Editor: Jose L Domingo Keywords: Relative humidity Spatiotemporal modeling Machine learning External validation Exposure assessment Environmental epidemiology A B S T R A C T The lack of readily available methods for estimating high-resolution near-surface relative humidity (RH) and the incapability of weather stations to fully capture the spatiotemporal variability can lead to exposure misclassification in studies of environmental epidemiology.



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