While it is obvious to any computational scientist that the performance of methods may depend on meta-features, this issue is not easy to investigate in real data settings because i) it requires a large number of datasets—a condition that is often not fulfilled in practice; ii) this problem is enhanced by the correlations between meta-features. In our benchmarking experiment, however, we consider such a huge number of datasets that an investigation of the relationship between methods’ performances and datasets’ characteristic becomes possible to some extent.
