DebiAI: Open-Source Toolkit for Data Analysis, Visualisation and Evaluation in Machine Learning - IRT SystemX Access content directly
Conference Papers Year : 2024

DebiAI: Open-Source Toolkit for Data Analysis, Visualisation and Evaluation in Machine Learning

Tom Mansion
  • Function : Author
  • PersonId : 1349356
Raphaël Braud
  • Function : Author
  • PersonId : 1349357
Ahmed Amrani
  • Function : Author
  • PersonId : 1349358
Sabrina Chaouche
  • Function : Author
  • PersonId : 1349359
Faouzi Adjed
Loïc Cantat
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  • PersonId : 751231
  • IdHAL : loic-cantat

Abstract

DebiAI is an open-source tool designed for data analysis, visualization, as well as evaluation and comparison of machine learning (ML) models. It is intended to be used both at the stage of the project data preparation, and for the evaluation of the ML models performances. It has a rich and user-friendly graphical interface that allows to visualize, analyze, select, edit and annotate data and metadata, as well as for bias detection and contextual evaluation of ML models. The tool relies on a generic data model, making it applicable to any type of ML task: classification, regression, object detection in images and more. It is an open source code distributed under the Apache License, Version 2.0. The code is publicly available (https://github.com/debiai) and further information along with guidelines for the users can be found on its dedicated website (https://debiai.irt-systemx.fr).
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licence : Copyright
Origin : Files produced by the author(s)
licence : Copyright

Dates and versions

hal-04446930 , version 1 (08-02-2024)

Identifiers

  • HAL Id : hal-04446930 , version 1

Cite

Tom Mansion, Raphaël Braud, Ahmed Amrani, Sabrina Chaouche, Faouzi Adjed, et al.. DebiAI: Open-Source Toolkit for Data Analysis, Visualisation and Evaluation in Machine Learning. 20th International Conference on Autonomic and Autonomous Systems (ICAS), Mar 2024, Athena, Greece. ⟨hal-04446930⟩
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