research-article

ModelTracker: Redesigning Performance Analysis Tools for Machine Learning

Authors:

Saleema Amershi,

Max Chickering,

Steven M. Drucker,

Patrice Simard,

Jina SuhAuthors Info & Claims

CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Pages 337 - 346

https://doi.org/10.1145/2702123.2702509

Published: 18 April 2015 Publication History

Abstract

Model building in machine learning is an iterative process. The performance analysis and debugging step typically involves a disruptive cognitive switch from model building to error analysis, discouraging an informed approach to model building. We present ModelTracker, an interactive visualization that subsumes information contained in numerous traditional summary statistics and graphs while displaying example-level performance and enabling direct error examination and debugging. Usage analysis from machine learning practitioners building real models with ModelTracker over six months shows ModelTracker is used often and throughout model building. A controlled experiment focusing on ModelTracker's debugging capabilities shows participants prefer ModelTracker over traditional tools without a loss in model performance.

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Cited By

Doppalapudi BRahman MRosen P(2025)Seeing Is Believing: The Role of Scatterplots in Recommender System Trust and Decision-MakingAdvances in Visual Computing10.1007/978-3-031-77392-1_32(425-438)Online publication date: 22-Jan-2025
https://doi.org/10.1007/978-3-031-77392-1_32
Zhang HYan BCao LMadden SRundensteiner E(2024)MetaStore: Analyzing Deep Learning Meta-Data at ScaleProceedings of the VLDB Endowment10.14778/3648160.364818217:6(1446-1459)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.14778/3648160.3648182
Ferdowsi MKwan BTan MSaedon NSubramaniam SAbu Hashim NMohd Nasir SZainal Abidin IChee KGoh C(2024)Classification of vasovagal syncope from physiological signals on tilt table testingBioMedical Engineering OnLine10.1186/s12938-024-01229-923:1Online publication date: 30-Mar-2024
https://doi.org/10.1186/s12938-024-01229-9
Show More Cited By

Index Terms

ModelTracker: Redesigning Performance Analysis Tools for Machine Learning
1. Human-centered computing
  1. Human computer interaction (HCI)

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Information

Published In

cover image ACM Conferences

CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

April 2015

4290 pages

ISBN:9781450331456

DOI:10.1145/2702123

General Chairs:
Bo Begole
Huawei, USA
,
Jinwoo Kim
Yonsei University, Korea
,
Program Chairs:
Kori Inkpen
Microsoft Research, USA
,
Woontack Woo
KAIST, Korea

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2015

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CHI '15

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SIGCHI

CHI '15: CHI Conference on Human Factors in Computing Systems

April 18 - 23, 2015

Seoul, Republic of Korea

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CHI '15 Paper Acceptance Rate 486 of 2,120 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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CHI 2025

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Cited By

Doppalapudi BRahman MRosen P(2025)Seeing Is Believing: The Role of Scatterplots in Recommender System Trust and Decision-MakingAdvances in Visual Computing10.1007/978-3-031-77392-1_32(425-438)Online publication date: 22-Jan-2025
https://doi.org/10.1007/978-3-031-77392-1_32
Zhang HYan BCao LMadden SRundensteiner E(2024)MetaStore: Analyzing Deep Learning Meta-Data at ScaleProceedings of the VLDB Endowment10.14778/3648160.364818217:6(1446-1459)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.14778/3648160.3648182
Ferdowsi MKwan BTan MSaedon NSubramaniam SAbu Hashim NMohd Nasir SZainal Abidin IChee KGoh C(2024)Classification of vasovagal syncope from physiological signals on tilt table testingBioMedical Engineering OnLine10.1186/s12938-024-01229-923:1Online publication date: 30-Mar-2024
https://doi.org/10.1186/s12938-024-01229-9
Schellaert WMartínez-Plumed FHernández-Orallo J(2024)Analysing the Predictability of Language Model PerformanceACM Transactions on Intelligent Systems and Technology10.1145/370611816:2(1-26)Online publication date: 28-Nov-2024
https://dl.acm.org/doi/10.1145/3706118
Fujita KUwaseki KBu HTakashima KKitamura Y(2024)ConfusionLens: Focus+Context Visualization Interface for Performance Analysis of Multiclass Image ClassifiersACM Transactions on Interactive Intelligent Systems10.1145/370013915:1(1-21)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1145/3700139
Peters HHashemi ARae J(2024)Generalizable Error Modeling for Human Data Annotation: Evidence From an Industry-Scale Search Data Annotation ProgramJournal of Data and Information Quality10.1145/368839416:3(1-15)Online publication date: 26-Sep-2024
https://dl.acm.org/doi/10.1145/3688394
Wan CLiu SXie SLiu YHoffmann HMaire MLu S(2024)Keeper: Automated Testing and Fixing of Machine Learning SoftwareACM Transactions on Software Engineering and Methodology10.1145/367245133:7(1-33)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3672451
Das Antar AMolaei SChen YLee MBanovic N(2024)VIME: Visual Interactive Model Explorer for Identifying Capabilities and Limitations of Machine Learning Models for Sequential Decision-MakingProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676323(1-21)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676323
Ma WYang CKästner CBosch JLewis GCleland-Huang JMuccini H(2024)(Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIsProceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI10.1145/3644815.3644950(166-171)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644815.3644950
Shome ACruz LVan Deursen AIzadi MDi Sorbo APanichella S(2024)Towards Automatic Translation of Machine Learning Visual Insights to Analytical AssertionsProceedings of the Third ACM/IEEE International Workshop on NL-based Software Engineering10.1145/3643787.3648032(29-32)Online publication date: 20-Apr-2024
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