From Black-Box Tuning to Guided Optimization via Hyperparameters Interaction Analysis

TL;DR

This paper introduces MetaSHAP, a scalable XAI method that analyzes hyperparameter interactions using meta-learning and Shapley values, providing interpretable importance scores and actionable tuning insights across large benchmarks.

Contribution

MetaSHAP is a novel method that offers dataset-aware hyperparameter importance and interaction analysis, enhancing model tuning efficiency and interpretability.

Findings

MetaSHAP accurately ranks hyperparameter importance across diverse datasets.

It guides Bayesian optimization with competitive performance.

Provides interpretable hyperparameter influence ranges.

Abstract

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.