  In machine learning (ML) workflows, determining the invariance qualities of
an ML model is a common testing procedure. Traditionally, invariance qualities
are evaluated using simple formula-based scores, e.g., accuracy. In this paper,
we show that testing the invariance qualities of ML models may result in
complex visual patterns that cannot be classified using simple formulas. In
order to test ML models by analyzing such visual patterns automatically using
other ML models, we propose a systematic framework that is applicable to a
variety of invariance qualities. We demonstrate the effectiveness and
feasibility of the framework by developing ML4ML models (assessors) for
determining rotation-, brightness-, and size-variances of a collection of
neural networks. Our testing results show that the trained ML4ML assessors can
perform such analytical tasks with sufficient accuracy.
