In this section, we now perform different types of additional analyses with the aim to investigate the relation between the datasets’ meta-features and the performance difference between LR and RF. In “Preliminary analysis” section, we first consider an example dataset in detail to examine whether changing the sample size n and the number p of features for this given dataset changes the difference between performances of LR and RF (focusing on a specific dataset, we are sure that confounding is not an issue). In “Subgroup analyses: meta-features” to “Meta-learning” sections, we then assess the association between dataset’s meta-features and performance difference over all datasets included in our study.
