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.

翻译：元学习是一个领域,旨在发现不同机器学习算法如何在广泛的预测性任务中发挥作用。这种知识加速了超参数调制或特征工程。使用代用模型的预测性任务的各个方面,如元地物,使用地标模型e.t.c.来预测预期的性能。最新的方法侧重于寻找最佳的元模型,但并不解释这些不同方面如何促进其性能。然而,为了建设新一代的元模型,我们需要更深入地了解超参数调或特征工程的重要性和效果。在本文件中,我们提出了为电子可扩展人工智能(XAI)开发的技术,以检查和提取黑盒代孕模型的知识。据我们所知,这是第一份文件,它展示了后热解能如何用来改进元学习。