Table 4 Comparative performance of ML models without hyperparameters optimization.

From: Machine learning models based wear performance prediction of AZ31/TiC composites

ML Regressor

R2

RMSE

MSE

MAE

CV Mean

CV SD

Linear Regression

0.7713

0.1166

0.0136

0.0792

0.7121

0.0370

Decision Tree

0.9945

0.0181

0.0003

0.0047

0.9856

0.0287

Random Forest

0.9919

0.0218

0.0005

0.0093

0.9827

0.0227

Gradient Boost

0.9952

0.0169

0.0003

0.0128

0.9866

0.0058

XGBoost

0.9967

0.0138

0.0002

0.0114

0.9896

0.0074

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