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Is there any grounds for assuming an algorithms applied to a data-set that created a decently accurate model will perform as well on a different data-set with meta-features chosen and evaluated by meta-learning? What meta-features are even worth considering when evaluating similarity between data-sets with the goal of finding an optimal combination of algorithm application to this new data-set to create an accurate model?

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