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Table 3 Performance metrics for the validation of ML models

From: Machine learning models for optimization, validation, and prediction of light emitting diodes with kinetin based basal medium for in vitro regeneration of upland cotton (Gossypium hirsutum L.)

ML models

R2

MSE

MAE

MAPE

MSLE

MedAE

RF

0.70

0.492

0.372

10.116

0.0201

0.078

XGBoost

0.69

0.515

0.414

10.976

0.0204

0.146

MLP

0.71

0.477

0.402

10.888

0.0194

0.137

  1. RF random forest, XGBoost extreme gradient boosting, MLP multilayer perceptron, R2 coefficient of determination, MSE mean squared error, MAE mean absolute error, MAPE mean absolute percentage error, MSLE mean square log error, MedAE median absolute error
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Journal of Cotton Research

ISSN: 2523-3254

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