From Black-Box Tuning to Guided Optimization via Hyperparameters Interaction Analysis
This addresses the problem of inefficient hyperparameter tuning for machine learning practitioners by offering a semi-automated explainable approach, though it is incremental as it builds on existing meta-learning and XAI techniques.
The paper tackles hyperparameter tuning by introducing MetaSHAP, a method that uses meta-learning and Shapley values to provide interpretable insights into hyperparameter importance and interactions, validated on 164 classification datasets and 14 classifiers to guide Bayesian optimization with competitive performance.
Hyperparameters tuning is a fundamental, yet computationally expensive, step in optimizing machine learning models. Beyond optimization, understanding the relative importance and interaction of hyperparameters is critical to efficient model development. In this paper, we introduce MetaSHAP, a scalable semi-automated eXplainable AI (XAI) method, that uses meta-learning and Shapley values analysis to provide actionable and dataset-aware tuning insights. MetaSHAP operates over a vast benchmark of over 09 millions evaluated machine learning pipelines, allowing it to produce interpretable importance scores and actionable tuning insights that reveal how much each hyperparameter matters, how it interacts with others and in which value ranges its influence is concentrated. For a given algorithm and dataset, MetaSHAP learns a surrogate performance model from historical configurations, computes hyperparameters interactions using SHAP-based analysis, and derives interpretable tuning ranges from the most influential hyperparameters. This allows practitioners not only to prioritize which hyperparameters to tune, but also to understand their directionality and interactions. We empirically validate MetaSHAP on a diverse benchmark of 164 classification datasets and 14 classifiers, demonstrating that it produces reliable importance rankings and competitive performance when used to guide Bayesian optimization.