  With the fast-growing number of classification models being produced every
day, numerous model interpretation and comparison solutions have also been
introduced. For example, LIME and SHAP can interpret what input features
contribute more to a classifier's output predictions. Different numerical
metrics (e.g., accuracy) can be used to easily compare two classifiers.
However, few works can interpret the contribution of a data feature to a
classifier in comparison with its contribution to another classifier. This
comparative interpretation can help to disclose the fundamental difference
between two classifiers, select classifiers in different feature conditions,
and better ensemble two classifiers. To accomplish it, we propose a
learning-from-disagreement (LFD) framework to visually compare two
classification models. Specifically, LFD identifies data instances with
disagreed predictions from two compared classifiers and trains a discriminator
to learn from the disagreed instances. As the two classifiers' training
features may not be available, we train the discriminator through a set of
meta-features proposed based on certain hypotheses of the classifiers to probe
their behaviors. Interpreting the trained discriminator with the SHAP values of
different meta-features, we provide actionable insights into the compared
classifiers. Also, we introduce multiple metrics to profile the importance of
meta-features from different perspectives. With these metrics, one can easily
identify meta-features with the most complementary behaviors in two
classifiers, and use them to better ensemble the classifiers. We focus on
binary classification models in the financial services and advertising industry
to demonstrate the efficacy of our proposed framework and visualizations.
