A Reference Process for Assessing the Reliability of Predictive Analytics Results
Simon Staudinger,
Christoph G. Schuetz,
Michael Schrefl
Abstract:Organizations employ data mining to discover patterns in historic data in order to learn predictive models. Depending on the predictive model the predictions may be more or less accurate, raising the question about the reliability of individual predictions. This paper proposes a reference process aligned with the CRISP-DM to enable the assessment of reliability of individual predictions obtained from a predictive model. The reference process describes activities along the different stages of the development pr… Show more
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