In , and are pivotal subpopulations, and other subpopulations such as and are considered to be derived from or . In this regard, accessions are frequently viewed from the perspective. This study introduces a computational method for classification by applying phenotypic variables to the logistic regression model (LRM). The population used in this study included 413 accessions, of which 280 accessions were or . Out of 24 phenotypic variables, a set of seven phenotypic variables was identified to collectively generate the fully accurate separation power of the LRM. The resulting parameters were used to define the customized LRM. Given the 280 accessions, the classification accuracy of the customized LRM along with the set of seven phenotypic variables was estimated by 100 iterations of ten-fold cross-validations. As a result, the classification accuracy of 100% was achieved. This suggests that the LRM can be an effective tool to analyze the classification with phenotypic variables in .
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| http://dx.doi.org/10.7717/peerj.7259 | DOI Listing |
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