Developing, evaluating and reviewing ML-based predictive model for numerical databases in civil engineering
Year: 2025
Type: article
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.
Author: Ahmed M. Ebid
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $250
OpenAlex ID: W4414884504