000			02075nab|a22003857a|4500
999			_c63538 _d63530
001			63538
003			MX-TxCIM
005			20240919021230.0
008			202102s2021||||xxu|||p|op||||00||0|eng|d
022			_a2160-1836 (Online)
024	8		_ahttps://doi.org/10.1093/g3journal/jkaa057
040			_aMX-TxCIM
041			_aeng
100	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
245	1	2	_aA zero altered Poisson random forest model for genomic-enabled prediction
260			_aBethesda, MD (USA) : _bGenetics Society of America, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aIn 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.
546			_aText in English
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aData _2AGROVOC _99002
650		7	_aPlant breeding _gAGROVOC _2 _91203
650		7	_aModels _2AGROVOC _94859
700	1		_aMontesinos-Lopez, A. _92702
700	1		_aMosqueda-Gonzalez, B.A. _919441
700	1		_aMontesinos-Lopez, J.C. _94950
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aLozano-Ramirez, N. _917230
700	1		_aPawan Kumar Singh _gGlobal Wheat Program _8INT2868 _9868
700	1		_aValladares-Anguiano, F.A. _919442
773	0		_tG3: Genes, Genomes, Genetics _gv. 11, no. 2, art. jkaa057 _dBethesda, MD (USA) : Genetics Society of America, 2021. _wu56922 _x2160-1836
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21342
942			_cJA _n0 _2ddc