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Predictability in ML Applications
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| by Claudia Perlich Follow 0 Followers on Mar 23, 2017 |
48:32

Summary
Claudia Perlich presents a number of scenarios in which the combination of different and highly informative features can have significantly negative overall impact on the usefulness of predictive modeling.

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Bio

Claudia Perlich currently acts as Chief Scientist at Distillery (previously m6d), and in this role designs, develops, analyzes, and optimizes the machine learning that drives digital advertising. She has published more than 50 scientific articles and holds multiple patents in machine learning.

Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in their teams.

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