Assessing the implementation of machine learning models for thermal treatments design
Adriana Eres-Castellanos, David De-Castro, C. Capdevila, Carlos García-Mateo, Francisca G. Caballero · Materials Science and Technology · 2021
The latest progress in machine learning (ML) algorithms enabled to predict some steel physical properties previously modelled by linear regression (LR), such as the Ms temperature. Authors claimed that the performance given by ML models could improve the one of previous LR models, although they did not include fair comparisons. In this work, a large database was used to train different ML algorithms, whose Ms temperature predictions were compared to the ones of previous literature empirical models. ML methods were proved to require longer computational times and wider knowledge, while leading to similar results. Therefore, we recommend that ML methods are not always considered as the first option when trying to solve easy problems that can be modelled by LR techniques.