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- Title
A zero altered Poisson random forest model for genomic-enabled prediction.
- 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
- 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
- Subjects
RANDOM forest algorithms; PREDICTION models; STATISTICAL learning; POISSON regression; MACHINE learning; PLANT breeding
- Publication
G3: Genes | Genomes | Genetics, 2021, Vol 11, Issue 2, p1
- ISSN
2160-1836
- Publication type
Article
- DOI
10.1093/g3journal/jkaa057