Table 6 ML models result on various metrics for wear.

From: Triboinformatic analysis and prediction of B4C and granite powder filled Al 6082 composites using machine learning regression models

Response

Metrics

MLR

SVM

RF

KNN

ANN

DT

XGBoost

FL

Wear

MSE

0.0612

0.0002

0.0031

0.0018

0.0004

0.0604

3.3659E-05

0.0003

MAPE (%)

24.970

7.3900

14.5244

13.005

8.5735

23.557

9.7746

6.2247

RMSE

0.2474

0.0138

0.0558

0.0425

0.0197

0.2457

0.0058

0.0171

MAE

0.0165

0.0007

0.0118

0.0087

0.0007

0.0218

0.0084

0.0023

\(\:{R}^{2}\)

0.6382

0.9563

0.8997

0.8975

0.9410

0.6502

0.9408

0.9638

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