TY  - JA
AU  - Montesinos-Lopez,O.A.
AU  - Montesinos-Lopez,A.
AU  - Mosqueda-Gonzalez,B.A.
AU  - Montesinos-Lopez,J.C.
AU  - Crossa,J.
AU  - Lozano-Ramirez,N.
AU  - Pawan Kumar Singh
AU  - Valladares-Anguiano,F.A.
TI  - A zero altered Poisson random forest model for genomic-enabled prediction
SN  - 2160-1836
PY  - 2021///
CY  - Bethesda, MD (USA)
PB  - Genetics Society of America, 
KW  - Marker-assisted selection
KW  - AGROVOC
KW  - Data
KW  - Plant breeding
KW  - Models
N1  - Peer review; Open Access
N2  - 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
UR  - https://hdl.handle.net/10883/21342
T2  - G3: Genes, Genomes, Genetics
DO  - https://doi.org/10.1093/g3journal/jkaa057
ER  -