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Table 4 Results for machine learning algorithm model accuracies developed using the complete set of iron deficiency chlorosis data on a diverse set of soybean accessions

From: A real-time phenotyping framework using machine learning for plant stress severity rating in soybean

Algorithm Accuracy MPCAa Cross validated MPCA Interpretability Cost metric
CT 99.7 91.7 78.4 Low 0.0027
Hierarchyb 99.2 90.7 79.2 High 0.0082
Hierarchyc 98.3 84.0 79.0 High 0.0201
QDA 98.5 83.2 77.9 Medium 0.0201
NB 98.4 79.0 78.5 Medium 0.0284
KNN 99.5 75.8 84.3 Low 0.0073
RF 99.1 75.0 81.1 Low 0.0092
GMMB 99.4 74.2 82.7 Low 0.0064
LDA 98.5 71.7 76.9 High 0.0156
SVM 97.3 45.8 45.3 Low 0.0458
  1. aMean per class accuracy
  2. bSVM: using SVM for both classifiers
  3. cLDA and SVM