  There are two things to be considered when we evaluate predictive models. One
is prediction accuracy,and the other is interpretability. Over the recent
decades, many prediction models of high performance, such as ensemble-based
models and deep neural networks, have been developed. However, these models are
often too complex, making it difficult to intuitively interpret their
predictions. This complexity in interpretation limits their use in many
real-world fields that require accountability, such as medicine, finance, and
college admissions. In this study, we develop a novel method called Meta-ANOVA
to provide an interpretable model for any given prediction model. The basic
idea of Meta-ANOVA is to transform a given black-box prediction model to the
functional ANOVA model. A novel technical contribution of Meta-ANOVA is a
procedure of screening out unnecessary interaction before transforming a given
black-box model to the functional ANOVA model. This screening procedure allows
the inclusion of higher order interactions in the transformed functional ANOVA
model without computational difficulties. We prove that the screening procedure
is asymptotically consistent. Through various experiments with synthetic and
real-world datasets, we empirically demonstrate the superiority of Meta-ANOVA
