A zero altered Poisson random forest model for genomic-enabled prediction
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.
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| Language: | English |
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Genetics Society of America
2021
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| Subjects: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Genomic Selection, Count Data, Random Forest, Zero Altered Poisson, Genomic Prediction, GenPred, Shared Data Resource, MARKER-ASSISTED SELECTION, DATA, PLANT BREEDING, MODELS, |
| Online Access: | https://hdl.handle.net/10883/21342 |
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dig-cimmyt-10883-213422023-11-15T14:52:04Z A zero altered Poisson random forest model for genomic-enabled prediction Montesinos-Lopez, O.A. Montesinos-Lopez, A. Mosqueda-Gonzalez, B.A. Montesinos-Lopez, J.C. Crossa, J. Lozano-Ramirez, N. Singh, P.K. Valladares-Anguiano, F.A. AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Count Data Random Forest Zero Altered Poisson Genomic Prediction GenPred Shared Data Resource MARKER-ASSISTED SELECTION DATA PLANT BREEDING MODELS 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. 2021-03-31T00:10:16Z 2021-03-31T00:10:16Z 2021 Article Published Version https://hdl.handle.net/10883/21342 10.1093/g3journal/jkaa057 English http://hdl.handle.net/11529/10575 http://hdl.handle.net/11529/10548438 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 CIMMYT-Knowledge-Center@cgiar.org 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 Open Access Bethesda, MD (USA) Genetics Society of America 2 11 2160-1836 G3: Genes, Genomes, Genetics jkaa057 |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Count Data Random Forest Zero Altered Poisson Genomic Prediction GenPred Shared Data Resource MARKER-ASSISTED SELECTION DATA PLANT BREEDING MODELS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Count Data Random Forest Zero Altered Poisson Genomic Prediction GenPred Shared Data Resource MARKER-ASSISTED SELECTION DATA PLANT BREEDING MODELS |
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AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Count Data Random Forest Zero Altered Poisson Genomic Prediction GenPred Shared Data Resource MARKER-ASSISTED SELECTION DATA PLANT BREEDING MODELS AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Count Data Random Forest Zero Altered Poisson Genomic Prediction GenPred Shared Data Resource MARKER-ASSISTED SELECTION DATA PLANT BREEDING MODELS Montesinos-Lopez, O.A. Montesinos-Lopez, A. Mosqueda-Gonzalez, B.A. Montesinos-Lopez, J.C. Crossa, J. Lozano-Ramirez, N. Singh, P.K. Valladares-Anguiano, F.A. A zero altered Poisson random forest model for genomic-enabled prediction |
| 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. |
| format |
Article |
| topic_facet |
AGRICULTURAL SCIENCES AND BIOTECHNOLOGY Genomic Selection Count Data Random Forest Zero Altered Poisson Genomic Prediction GenPred Shared Data Resource MARKER-ASSISTED SELECTION DATA PLANT BREEDING MODELS |
| author |
Montesinos-Lopez, O.A. Montesinos-Lopez, A. Mosqueda-Gonzalez, B.A. Montesinos-Lopez, J.C. Crossa, J. Lozano-Ramirez, N. Singh, P.K. Valladares-Anguiano, F.A. |
| author_facet |
Montesinos-Lopez, O.A. Montesinos-Lopez, A. Mosqueda-Gonzalez, B.A. Montesinos-Lopez, J.C. Crossa, J. Lozano-Ramirez, N. Singh, P.K. Valladares-Anguiano, F.A. |
| author_sort |
Montesinos-Lopez, O.A. |
| title |
A zero altered Poisson random forest model for genomic-enabled prediction |
| title_short |
A zero altered Poisson random forest model for genomic-enabled prediction |
| title_full |
A zero altered Poisson random forest model for genomic-enabled prediction |
| title_fullStr |
A zero altered Poisson random forest model for genomic-enabled prediction |
| title_full_unstemmed |
A zero altered Poisson random forest model for genomic-enabled prediction |
| title_sort |
zero altered poisson random forest model for genomic-enabled prediction |
| publisher |
Genetics Society of America |
| publishDate |
2021 |
| url |
https://hdl.handle.net/10883/21342 |
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