Table 3 Results for machine learning algorithm model accuracies developed using a sub-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 | 100.0 | 100.0 | 96.0 | Medium | 0.0000 |
KNN | 99.7 | 96.7 | 95.0 | Low | 0.0031 |
RF | 99.7 | 96.0 | 85.0 | Low | 0.0031 |
Hierarchyb | 99.4 | 95.9 | 79.8 | High | 0.0062 |
QDA | 99.4 | 92.0 | 98.9 | Medium | 0.0620 |
Hierarchyc | 98.5 | 86.6 | 70.8 | High | 0.0155 |
GMMB | 99.1 | 82.0 | 87.0 | Medium | 0.0093 |
NB | 99.1 | 82.0 | 93.8 | Medium | 0.0093 |
LDA | 98.8 | 79.3 | 84.3 | High | 0.0124 |
SVM | 93.8 | 39.8 | 50.0 | Low | 0.1084 |