Developing, evaluating and reviewing ML-based predictive model for numerical databases in civil engineering | ||
| JES. Journal of Engineering Sciences | ||
| Articles in Press, Accepted Manuscript, Available Online from 30 September 2025 | ||
| Document Type: Research Paper | ||
| DOI: 10.21608/jesaun.2025.396632.1566 | ||
| Author | ||
| Ahmed M. Ebid* | ||
| Future University in Egypt | ||
| Abstract | ||
| Over the past decade, the application of machine learning techniques in both industrial and academic projects has become common practice. During this period, hundreds of machine learning-based predictive models have been developed for various topics using various machine learning techniques. Unfortunately, some of these models are inefficient, inaccurate, or unusable. This is due to a lack of data science knowledge among some authors or even reviewers. This paper aims to provide guidance for authors on model development and evaluation, and a checklist for reviewers on machine learning-based predictive models. The paper discusses in detail the basic steps for developing, evaluating, and reviewing machine learning-based predictive models. These steps include: data collection (size, type, and source); data preprocessing, forming a valid database, and partitioning; statistical analysis, correlation, and sensitivity; technique selection, model training, and performance evaluation; and model comparison, discussion of results, and drawing conclusions. In addition, the paper provides an overview of available research quality checklists and proposes a more detailed and general checklist for evaluating and reviewing predictive models. | ||
| Keywords | ||
| Predictive models; machine learning; Data science; Model evaluation; Numerical databases | ||
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