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Current unsupervised feature selection evaluations are often misleading, masking supervised influences that compromise their validity.
Recursive feature elimination outperforms traditional global ranking methods, revealing hidden insights in noisy datasets that could transform feature selection practices.
Stop reinventing the wheel: FSEVAL provides a standardized toolbox and dashboard to streamline the evaluation of feature selection algorithms, saving researchers time and promoting rigorous comparisons.
