# HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models

arXiv: https://arxiv.org/abs/2002.05271 · HTML: https://mlsanity.com/paper/2002.05271/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods](https://mlsanity.com/paper/2302.06949/) (2023)
- [iGAiVA: Integrated Generative AI and Visual Analytics in a Machine Learning Workflow for Text Classification](https://mlsanity.com/paper/2409.15848/) (2024)
- [Trinary Tools for Continuously Valued Binary Classifiers](https://mlsanity.com/paper/2204.08136/) (2022)
- [Understanding How Humans Inject Knowledge into Machine Learning Workflows through Visual Analytics](https://mlsanity.com/paper/2607.00969/) (2026)

## Abstract (arXiv)

In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models. We describe a novel ML-testing framework that combines the traditional statistical hypothesis testing (commonly used in empirical research) with logical reasoning about the conclusions of multiple hypotheses. The framework defines a controlled configuration for testing a number of hypotheses as to whether and how some extra information about a "concept" or "feature" may benefit or hinder a ML model. Because reasoning multiple hypotheses is not always straightforward, we provide HypoML as a visual analysis tool, with which, the multi-thread testing data is transformed to a visual representation for rapid observation of the conclusions and the logical flow between the testing data and hypotheses.We have applied HypoML to a number of hypothesized concepts, demonstrating the intuitive and explainable nature of the visual analysis.