  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.
