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A zero altered Poisson random forest model for genomic-enabled prediction

Creator: Montesinos-Lopez, O.A.
Creator: Montesinos-López, A.
Creator: Mosqueda-Gonzalez, B.A.
Creator: Montesinos-Lopez, J.C.
Creator: Crossa, J.
Creator: Lozano-Ramirez, N.
Creator: Singh, P.K.
Creator: Valladares-Anguiano, F.A.
Year: 2021
Language: English
Publisher: Genetics Society of America
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: Bethesda, MD (USA)
Issue: 2
Volume: 11
DOI: 10.1093/g3journal/jkaa057
Keywords: Genomic Selection
Keywords: Count Data
Keywords: Random Forest
Keywords: Zero Altered Poisson
Keywords: Genomic Prediction
Keywords: GenPred
Keywords: Shared Data Resource
Description: In genomic selection choosing the statistical machine learning model is of paramount importance. In this paper, we present an application of a zero altered random forest model with two versions (ZAP_RF and ZAPC_RF) to deal with excess zeros in count response variables. The proposed model was compared with the conventional random forest (RF) model and with the conventional Generalized Poisson Ridge regression (GPR) using two real datasets, and we found that, in terms of prediction performance, the proposed zero inflated random forest model outperformed the conventional RF and GPR models.
Agrovoc: DATA
Agrovoc: MODELS
Related Datasets:
Related Datasets:
ISSN: 2160-1836
Journal: G3: Genes, Genomes, Genetics
Article number: jkaa057

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  • Genetic Resources
    Genetic Resources including germplasm collections, wild relatives, genotyping, genomics, and IP
  • Wheat
    Wheat - breeding, phytopathology, physiology, quality, biotech

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