Introduction One important thing in machine learning is feature engineering (selection & extraction). This means choosing the right variables that improve the model’s performance, while discarding those reducing it. The more impact your variables have on the performance metric, the better. Because the real world is complex, you may start with dozens or even hundreds of variables (=features), but in…
Associate Professor (tenure track) at the University of Vaasa, and Adjunct Professor at the Turku School of Economics. Based in Finland.