Towards explainable meta-learning
This work addresses the need for explainability in meta-learning to enhance model tunability, representing an incremental step by applying existing XAI methods to a new context.
The paper tackles the problem of understanding how different aspects like meta-features contribute to performance in meta-learning, and it proposes using explainable AI techniques to extract knowledge from black-box surrogate models, showing this can improve meta-learning.
Meta-learning is a field that aims at discovering how different machine learning algorithms perform on a wide range of predictive tasks. Such knowledge speeds up the hyperparameter tuning or feature engineering. With the use of surrogate models various aspects of the predictive task such as meta-features, landmarker models e.t.c. are used to predict the expected performance. State of the art approaches are focused on searching for the best meta-model but do not explain how these different aspects contribute to its performance. However, to build a new generation of meta-models we need a deeper understanding of the importance and effect of meta-features on the model tunability. In this paper, we propose techniques developed for eXplainable Artificial Intelligence (XAI) to examine and extract knowledge from black-box surrogate models. To our knowledge, this is the first paper that shows how post-hoc explainability can be used to improve the meta-learning.