1 Scope
This document establishes general common organizational approaches, regardless of
the type, size or nature of the applying organization, to ensure data quality for
training and evaluation in analytics and machine learning (ML). It includes guidance
on the data quality process for:
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— supervised ML with regard to the labelling of data used for training ML systems, including common organizational approaches for training data labelling;
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— unsupervised ML;
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— semi-supervised ML;
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— reinforcement learning;
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— analytics.
This document is applicable to training and evaluation data that come from different
sources, including data acquisition and data composition, data preparation, data labelling,
evaluation and data use. This document does not define specific services, platforms
or tools.