Towards explainable meta-learning

• URL: http://arxiv.org/abs/2002.04276v2
• Date: Mon, 12 Jul 2021 12:23:23 GMT
• Title: Towards explainable meta-learning
• Authors: Katarzyna Wo\'znica and Przemys{\l}aw Biecek
• Abstract summary: Meta-learning aims at discovering how different machine learning algorithms perform on a wide range of predictive tasks. 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. We propose techniques developed for eXplainable Artificial Intelligence (XAI) to examine and extract knowledge from black-box surrogate models.
• Score: 5.802346990263708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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