ISO/IEC TS 4213:2022

Information technology — Artificial intelligence — Assessment of machine learning classification performance

This document specifies methodologies for measuring classification performance of machine learning models, systems and algorithms.

Technologies de l'information — Intelligence artificielle — Evaluation des performances de classification de l'apprentissage machine

General Information

• Status: Published
• Publication Date: 12-Oct-2022
• ICS: 35.020 - Information technology (IT) in general
• Technical Committee: ISO/IEC JTC 1/SC 42 - Artificial intelligence
• Drafting Committee: ISO/IEC JTC 1/SC 42/WG 5 - Computational approaches and computational characteristics of AI systems
• Current Stage: 9092 - International Standard to be revised
• Completion Date: 01-May-2024

Standards Content (Sample)

TECHNICAL ISO/IEC TS
SPECIFICATION 4213
First edition
2022-10
Information technology — Artificial
intelligence — Assessment of machine
learning classification performance
Technologies de l'information — Intelligence artificielle — Evaluation
des performances de classification de l'apprentissage machine
Reference number
ISO/IEC TS 4213:2022(E)
© ISO/IEC 2022

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ISO/IEC TS 4213:2022(E)
COPYRIGHT PROTECTED DOCUMENT
© ISO/IEC 2022
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
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ISO copyright office
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Published in Switzerland
ii
  © ISO/IEC 2022 – All rights reserved

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ISO/IEC TS 4213:2022(E)
Contents Page
Foreword .v
Introduction . vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
3.1 Classification and related terms . 1
3.2 Metrics and related terms . 1
4 Abbreviated terms . 3
5 General principles . 4
5.1 Generalized process for machine learning classification performance assessment . 4
5.2 Purpose of machine learning classification performance assessment . 4
5.3 Control criteria in machine learning classification performance assessment . 5
5.3.1 General . 5
5.3.2 Data representativeness and bias . 5
5.3.3 Preprocessing. 5
5.3.4 Training data . . 5
5.3.5 Test and validation data . 6
5.3.6 Cross-validation . 6
5.3.7 Limiting information leakage . 6
5.3.8 Limiting channel effects . 6
5.3.9 Ground truth .
...