Details

Autor(en) / Beteiligte

• Kanter, James Max
• Gillespie, Owen
• Veeramachaneni, Kalyan

Titel

Label, Segment, Featurize: A Cross Domain Framework for Prediction Engineering

Ist Teil von

• 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016, p.430-439

Ort / Verlag

IEEE

Erscheinungsjahr

2016

Link zum Volltext

Quelle

IEEE Xplore

Beschreibungen/Notizen

• In this paper, we introduce "prediction engineering" as a formal step in the predictive modeling process. We define a generalizable 3 part framework - Label, Segment, Featurize (L-S-F) - to address the growing demand for predictive models. The framework provides abstractions for data scientists to customize the process to unique prediction problems. We describe how to apply the L-S-F framework to characteristic problems in 2 domains and demonstrate an implementation over 5 unique prediction problems defined on a dataset of crowdfunding projects from DonorsChoose.org. The results demonstrate how the L-S-F framework complements existing tools to allow us to rapidly build and evaluate 26 distinct predictive models. L-S-F enables development of models that provide value to all parties involved (donors, teachers, and people running the platform).