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Machine learning model for flower color prediction accuracy in P. grandiflorus. Receiver operating characteristic (ROC) curves for 9 SNPs (FDR < 0.05) using six machine learning models. Each values of area under the curve (AUC) shows the average of 10 cross validations for pink, violet, and white flowers. The six models used are support vector machine (SVM), k-neural network (k-NN), random forest (RF), C5.0 decision tree (C5.0), partial least square (PLS), and gradient boosting (GBM).
