Hyperparameter Importance & Interpretability: Rethinking Bias-Variance with Meta-features

In the context of solving classification tasks, model performance often faces challenges related to balancing Bias and Variance errors. It is commonly suggested that fine-tuning model hyperparameters can significantly enhance classification accuracy. However, we propose that a more substantial performance boost can be achieved by focusing on the tuning of dataset meta-features.