Journal
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021, PT I
Volume 1524, Issue -, Pages 505-520Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-93736-2_38
Keywords
Meta-learning; Explainable artificial intelligence; OpenML
Funding
- NCN Opus grant [2017/27/B/ST6/01307]
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Meta-learning is a field that explores the performance of different machine learning algorithms on predictive tasks and aims to accelerate hyperparameter tuning or feature engineering. Current research focuses on finding the best meta-model without explaining how different aspects contribute to its performance. To build new generation of meta-models, a deeper understanding of the importance and effect of meta-features on model tunability is needed.
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, etc., are used to predict expected performance. State-of-the-art approaches focus 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 model tunability. This paper proposes 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 meta-learning.
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