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
Current Stage
9092 - International Standard to be revised
Completion Date
01-May-2024
Ref Project

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Technical specification
ISO/IEC TS 4213:2022 - Information technology — Artificial intelligence — Assessment of machine learning classification performance Released:13. 10. 2022
English language
33 pages
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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
or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Email: copyright@iso.org
Website: www.iso.org
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 .
...

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