  Meta-learning is used to efficiently enable the automatic selection of
machine learning models by combining data and prior knowledge. Since the
traditional meta-learning technique lacks explainability, as well as
shortcomings in terms of transparency and fairness, achieving explainability
for meta-learning is crucial. This paper proposes FIND, an interpretable
meta-learning framework that not only can explain the recommendation results of
meta-learning algorithm selection, but also provide a more complete and
accurate explanation of the recommendation algorithm's performance on specific
datasets combined with business scenarios. The validity and correctness of this
framework have been demonstrated by extensive experiments.
