A zero altered Poisson random forest model for genomic-enabled prediction. Issue 2 (21st December 2020)

• Record Type: Journal Article
• Authors: Montesinos-López, Osval Antonio; Montesinos-López, Abelardo; Mosqueda-Gonzalez, Brandon A; Montesinos-López, José Cricelio; Crossa, José; Ramirez, Nerida Lozano; Singh, Pawan; Valladares-Anguiano, Felícitas Alejandra
• Editors: Akhunov, E
• Is Part Of: G3. Volume 11:Issue 2(2021)
• Publication Date: 2020-12-21
• Subjects: genomic selection -- count data -- random forest -- zero altered Poisson -- plant breeding -- Genomic Prediction -- GenPred -- Shared Data Resource; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1093/g3journal/jkaa057
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: 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.