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Most feature selection benchmarks are misleading; tree-based methods and F-Test emerge as surprisingly robust and efficient choices on real-world tabular data.
Forget individual functions – the symmetries of low-dimensional landscapes reveal only 12,007 fundamentally different optimization problems for rank-based algorithms.
