mlsanityData to 14 Oct 2026

Machine Learning Prescriptive Canvas for Optimizing Business Outcomes

arXiv 2206.10333 ↗First posted 21 Jun 2022cs.LG stat.ME stat.MLcited by 0

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Abstract (arXiv)

Data science has the potential to improve business in a variety of verticals. While the lion's share of data science projects uses a predictive approach, to drive improvements these predictions should become decisions. However, such a two-step approach is not only sub-optimal but might even degrade performance and fail the project. The alternative is to follow a prescriptive framing, where actions are "first citizens" so that the model produces a policy that prescribes an action to take, rather than predicting an outcome. In this paper, we explain why the prescriptive approach is important and provide a step-by-step methodology: the Prescriptive Canvas. The latter aims to improve framing and communication across the project stakeholders including project and data science managers towards a successful business impact.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 40 references that are in the map. First posted on arXiv 21 Jun 2022.

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12 of 40 references.

References · 12

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