# Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective

arXiv: https://arxiv.org/abs/1702.01226 · HTML: https://mlsanity.com/paper/1702.01226/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2017.

## Built on by (most cited first, 30 of 36)

- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization](https://mlsanity.com/paper/2204.06504/) (2022)
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- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Explainable Machine Learning with Prior Knowledge: An Overview](https://mlsanity.com/paper/2105.10172/) (2021)
- [Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework](https://mlsanity.com/paper/2201.07849/) (2022)
- [Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions](https://mlsanity.com/paper/2107.09546/) (2021)
- [Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles](https://mlsanity.com/paper/2103.10229/) (2021)
- [Interactive Machine Learning: A State of the Art Review](https://mlsanity.com/paper/2207.06196/) (2022)
- [iScore: Visual Analytics for Interpreting How Language Models Automatically Score Summaries](https://mlsanity.com/paper/2403.04760/) (2024)
- [Visual Explanations with Attributions and Counterfactuals on Time Series Classification](https://mlsanity.com/paper/2307.08494/) (2023)
- [Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates](https://mlsanity.com/paper/2107.04974/) (2021)
- [Efficient Human-in-the-loop System for Guiding DNNs Attention](https://mlsanity.com/paper/2206.05981/) (2022)
- [VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations](https://mlsanity.com/paper/2104.02797/) (2021)
- [Visualization Techniques to Enhance Automated Event Extraction](https://mlsanity.com/paper/2106.06588/) (2021)
- [An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury](https://mlsanity.com/paper/2208.06717/) (2022)
- [AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models](https://mlsanity.com/paper/2101.11970/) (2021)
- [Visualization for Recommendation Explainability: A Survey and New Perspectives](https://mlsanity.com/paper/2305.11755/) (2023)
- [iGAiVA: Integrated Generative AI and Visual Analytics in a Machine Learning Workflow for Text Classification](https://mlsanity.com/paper/2409.15848/) (2024)
- [RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systems](https://mlsanity.com/paper/2601.12991/) (2026)
- [Multi-point dimensionality reduction to improve projection layout reliability](https://mlsanity.com/paper/2101.06224/) (2021)
- [Novelty Detection in Sequential Data by Informed Clustering and Modeling](https://mlsanity.com/paper/2103.03943/) (2021)
- [Proposing an Interactive Audit Pipeline for Visual Privacy Research](https://mlsanity.com/paper/2111.03984/) (2021)
- [GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generation](https://mlsanity.com/paper/2308.13588/) (2023)
- [TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic](https://mlsanity.com/paper/2403.04812/) (2024)
- [A Rigorous Behavior Assessment of CNNs Using a Data-Domain Sampling Regime](https://mlsanity.com/paper/2507.03866/) (2025)
- [NeuroBreak: Unveil Internal Jailbreak Mechanisms in Large Language Models](https://mlsanity.com/paper/2509.03985/) (2025)
- [ConceptViz: A Visual Analytics Approach for Exploring Concepts in Large Language Models](https://mlsanity.com/paper/2509.20376/) (2025)
- [Trust Calibration as a Function of the Evolution of Uncertainty in Knowledge Generation: A Survey](https://mlsanity.com/paper/2209.04388/) (2022)

## Abstract (arXiv)

Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics has led to a wide variety of interactive model analysis tasks. In this paper, we present a comprehensive analysis and interpretation of this rapidly developing area. Specifically, we classify the relevant work into three categories: understanding, diagnosis, and refinement. Each category is exemplified by recent influential work. Possible future research opportunities are also explored and discussed.