Table 1 Regression evaluation metrics of performed algorithms on the experimental data (cross-validation with K-fold = 5).

From: Facile and highly precise pH-value estimation using common pH paper based on machine learning techniques and supported mobile devices

 

MSE

RMSE

MAE

R2

CVRMSE

kNN

0.071

0.266

0.160

0.995

3.385

AdaBoost

0.116

0.341

0.157

0.992

4.332

Gradient Boosting

0.130

0.361

0.242

0.991

4.590

Random Forest

0.164

0.405

0.196

0.988

5.143

Neural Network

0.794

0.891

0.578

0.943

11.321

SVM

3.584

1.893

1.460

0.744

24.058

Ridge regression

4.399

2.097

1.637

0.686

26.651

Linear Regression

4.399

2.097

1.637

0.686

26.651

Elastic net regression

4.399

2.097

1.637

0.686

26.651

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