Skip to Main content Skip to Navigation
New interface
Conference papers

ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classifiers

Abstract : A major challenge during the development of Machine Learning systems is the large number of models resulting from testing different model types, parameters, or feature subsets. The common approach of selecting the best model using one overall metric does not necessarily find the most suitable model for a given application, since it ignores the different effects of class confusions. Expert knowledge is key to evaluate, understand and compare model candidates and hence to control the training process. This paper addresses the research question of how we can support experts in the evaluation and selection of Machine Learning models, alongside the reasoning about them. ML-ModelExplorer is proposed – an explorative, interactive, and model-agnostic approach utilising confusion matrices. It enables Machine Learning and domain experts to conduct a thorough and efficient evaluation of multiple models by taking overall metrics, per-class errors, and individual class confusions into account. The approach is evaluated in a user-study and a real-world case study from football (soccer) data analytics is presented.ML-ModelExplorer and a tutorial video are available online for use with own data sets: www.ml-and-vis.org/mex
Complete list of metadata

https://hal.inria.fr/hal-03414731
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, November 4, 2021 - 3:57:27 PM
Last modification on : Friday, November 5, 2021 - 3:58:03 AM
Long-term archiving on: : Saturday, February 5, 2022 - 7:08:16 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Andreas Theissler, Simon Vollert, Patrick Benz, Laurentius A. Meerhoff, Marc Fernandes. ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classifiers. 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2020, Dublin, Ireland. pp.281-300, ⟨10.1007/978-3-030-57321-8_16⟩. ⟨hal-03414731⟩

Share

Metrics

Record views

23