# Boxer: Interactive Comparison of Classifier Results

arXiv: https://arxiv.org/abs/2004.07964 · HTML: https://mlsanity.com/paper/2004.07964/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 28 of 28)

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- [Calibrate: Interactive Analysis of Probabilistic Model Output](https://mlsanity.com/paper/2207.13770/) (2022)
- [Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments](https://mlsanity.com/paper/2408.03274/) (2024)
- [AI-Spectra: A Visual Dashboard for Model Multiplicity to Enhance Informed and Transparent Decision-Making](https://mlsanity.com/paper/2411.10490/) (2024)
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- [IMACS: Image Model Attribution Comparison Summaries](https://mlsanity.com/paper/2201.11196/) (2022)
- [VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions](https://mlsanity.com/paper/2307.14448/) (2023)
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- [Visual Identification of Problematic Bias in Large Label Spaces](https://mlsanity.com/paper/2201.06386/) (2022)
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- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Trinary Tools for Continuously Valued Binary Classifiers](https://mlsanity.com/paper/2204.08136/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [A Comparative Visual Analytics Framework for Evaluating Evolutionary Processes in Multi-objective Optimization](https://mlsanity.com/paper/2308.05640/) (2023)
- [MLMC: Interactive multi-label multi-classifier evaluation without confusion matrices](https://mlsanity.com/paper/2501.14460/) (2025)

## Abstract (arXiv)

Machine learning practitioners often compare the results of different classifiers to help select, diagnose and tune models. We present Boxer, a system to enable such comparison. Our system facilitates interactive exploration of the experimental results obtained by applying multiple classifiers to a common set of model inputs. The approach focuses on allowing the user to identify interesting subsets of training and testing instances and comparing performance of the classifiers on these subsets. The system couples standard visual designs with set algebra interactions and comparative elements. This allows the user to compose and coordinate views to specify subsets and assess classifier performance on them. The flexibility of these compositions allow the user to address a wide range of scenarios in developing and assessing classifiers. We demonstrate Boxer in use cases including model selection, tuning, fairness assessment, and data quality diagnosis.