Towards better understanding of meta-features contributions
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Meta learning is a difficult problem as the expected performance of a model is affected by various aspects such as selected hyperparameters, dataset properties and landmarkers. Existing approaches are focused on searching for the best model but do not explain how these different aspects contribute to the performance of a model. To build a new generation of meta-models we need a deeper understanding of relative importance of meta-features and construction of better meta-features. In this paper we (1) introduce a method that can be used to understand how meta-features influence a model performance, (2) discuss the relative importance of different groups of meta-features, (3) analyse in detail the most informative hyperparameters that may result in insights for selection of empirical priors of engineering for hyperparameters. To our knowledge this is the first paper that uses techniques developed for eXplainable Artificial Intelligence (XAI) to examine the behaviour of a meta model.
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