Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework

TL;DR

This paper introduces a visual analytics framework called Learning-From-Disagreement (LFD) for comparing two classifiers by analyzing their disagreements and meta-features, providing insights into their differences and complementarity.

Contribution

The paper proposes a novel LFD framework that interprets classifier differences through disagreement analysis, meta-feature profiling, and SHAP-based explanations, enhancing model comparison and ensemble strategies.

Findings

Effectively identifies key meta-features differentiating classifiers.

Provides actionable insights for classifier ensemble improvement.

Demonstrates framework efficacy in financial and advertising domains.

Abstract

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.