arXiv Open Access 2022

Machine Learning Prescriptive Canvas for Optimizing Business Outcomes

Hanan Shteingart Gerben Oostra Ohad Levinkron Naama Parush Gil Shabat +1 lainnya

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Abstrak

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.

Topik & Kata Kunci

cs.LG stat.ME stat.ML

Penulis (6)

Hanan Shteingart

Gerben Oostra

Ohad Levinkron

Naama Parush

Gil Shabat

Daniel Aronovich

Format Sitasi

APA MLA BibTeX

Shteingart, H., Oostra, G., Levinkron, O., Parush, N., Shabat, G., Aronovich, D. (2022). Machine Learning Prescriptive Canvas for Optimizing Business Outcomes. https://arxiv.org/abs/2206.10333

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓