Machine learning for corrosion database

This database was firstly created for the scientific article entitled:
"Reviewing Machine Learning of corrosion prediction: a data-oriented perspective"

L.B. Coelho 1 , D. Zhang 2 , Y.V. Ingelgem 1 , D. Steckelmacher 3 , A. Nowé 3 , H.A. Terryn 1

1 Department of Materials and Chemistry, Research Group Electrochemical and Surface Engineering, Vrije Universiteit Brussel, Brussels, Belgium
2 A Beijing Advanced Innovation Center for Materials Genome Engineering, National Materials Corrosion and Protection Data Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China
3 VUB Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium

Different metrics are possible to evaluate the prediction accuracy of regression models. However, only papers providing relative metrics (MAPE, R²) were included in this database. We tried as much as possible to include descriptors of all major ML procedure steps, including data collection (“Data acquisition”), data cleaning feature engineering (“Feature reduction”), model validation (“Train-Test split”*), etc.

*the total dataset is typically split into training sets and testing (unknown data) sets for performance evaluation of the model. Nonetheless, sometimes only the training or the testing performances were reported (“?” marks were added in the respective evaluation metric field(s)). The “Average R²” was sometimes considered for studies employing “CV” (cross-validation) on the dataset. For a detailed description of the ML basic procedures, the reader could refer to the References topic in the Review article.