Taking another perspective on the problem of benchmarking results being dependent on dataset’s meta-features, we also consider modelling the difference between the methods’ performances (considered as response variable) based on the datasets’ meta-features (considered as features). Such a modelling approach can be seen as a simple form of meta-learning—a well-known task in machine learning [29]. A similar approach using linear mixed models has been recently applied to the selection of an appropriate classification method in the context of high-dimensional gene expression data analysis [30]. Considering the potentially complex dependency patterns between response and features, we use RF as a prediction tool for this purpose.
