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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| Main Authors: | , , , , , , , |
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| Format: | Article biblioteca |
| Language: | English |
| Published: |
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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