Evaluating ML pipelines for real – spoiler alert: another pipeline (article review)

Evaluating classifiers in SE research: the ECSER pipeline and two replication studies (springer.com)

BIld av paula bassi från Pixabay

One of the most prominent problems with using research results in practice is the lack of replication packages, but this is far from being the only one. Another one, maybe an equally important problem, is the fact that the studies report performance in many different ways.

Since I have a chance to work with colleagues in medicine, I got to learn about their publication culture. It is more advanced than ours (software engineering), but that’s not the point. The main point is that they actually have guidelines on how to report ML studies. Here is an example of such a guideline: Clinician checklist for assessing suitability of machine learning applications in healthcare – PMC (nih.gov)

The paper that I wish to bring up is trying to address a similar aspect of software engineering. The paper reviews existing studies that provide recommendations, e.g., to report confusion matrices or to report statistical significance tests. Then it reviews some of the papers published in respected venues and then it provides actionable guidelines on how to evaluate the performance of machine learning models.

Author: Miroslaw Staron

I’m professor in Software Engineering at IT faculty. I usually blog about interesting articles (for me) and my own reflections on the development of Software Engineering, AI, computer science and automotive software.