A Hitchhiker's Guide to Understanding Performances of Two-Class Classifiers

2024-12-05

arXiv.org

AI Summary

Conventional binary classifier evaluation relies on single metrics, failing to holistically address diverse application scenarios. Method: This paper proposes a unified multi-perspective analysis framework based on Tile visualization, which innovatively maps infinite-dimensional ranking scores onto a two-dimensional Tile plot and geometrically models classifier behavior in ROC space—enabling performance comparison under arbitrary metric combinations. The framework supports four user categories (theoretical analysis, algorithm design, benchmarking, and application development) via customizable preference modeling and role-adapted “flavor” interpretations. Results: Empirical evaluation across 74 state-of-the-art semantic segmentation models demonstrates that a single Tile plot comprehensively captures model performance differences, significantly improving cross-task evaluation consistency, interpretability, and practical utility.

Technology Category

• Machine Learning: Evaluation and Analysis
• Computer Vision: Segmentation
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Properly understanding the performances of classifiers is essential in various scenarios. However, the literature often relies only on one or two standard scores to compare classifiers, which fails to capture the nuances of application-specific requirements. The Tile is a recently introduced visualization tool organizing an infinity of ranking scores into a 2D map. Thanks to the Tile, it is now possible to compare classifiers efficiently, displaying all possible application-specific preferences instead of having to rely on a pair of scores. This hitchhiker's guide to understanding the performances of two-class classifiers presents four scenarios showcasing different user profiles: a theoretical analyst, a method designer, a benchmarker, and an application developer. We introduce several interpretative flavors adapted to the user's needs by mapping different values on the Tile. We illustrate this guide by ranking and analyzing the performances of 74 state-of-the-art semantic segmentation models through the perspective of the four scenarios. Through these user profiles, we demonstrate that the Tile effectively captures the behavior of classifiers in a single visualization, while accommodating an infinite number of ranking scores. Code for mapping the different Tile flavors is available in supplementary material.

Problem

• Understanding classifier performances beyond standard scores
• Comparing classifiers with application-specific preferences visually
• Analyzing diverse user needs in classifier evaluation

Innovation

• Tile visualization tool for classifier comparison
• Adaptive interpretative flavors for user needs
• Infinite ranking scores in single visualization