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Tuesday, October 16 • 4:00pm - 4:40pm
The secret life of predictive models

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In the context of building predictive models, predictability is usually considered a blessing. After all – that is the goal: build the model that has the highest predictive performance. The rise of ‘big data’ has in fact vastly improved our ability to predict human behavior thanks to the introduction of much more informative features. However, in practice things are more differentiated than that. For many applications, the relevant outcome is observed for very different reasons. In such mixed scenarios, the model will automatically gravitate to the one that is easiest to predict at the expense of the others. This even holds if the predictable scenario is by far less common or relevant. We present a number of applications where this happens: clicks on ads being performed ‘intentionally’ vs. ‘accidentally’, consumers visiting store locations vs. their phones pretending to be there, and finally customers filling out online forms vs. bots defrauding the advertising industry. In conclusion, the combination of different and highly informative features can have significantly negative impact on the usefulness of predictive modeling and potentially create second order biased in the predictions.

Speakers
avatar for Claudia Perlich

Claudia Perlich

SVP & Senior Data Scientist, Two Sigma
Claudia Perlich is a Senior Data Scientist at Two Sigma in New York City. Prior to her role at Two Sigma, she was the Chief Scientist at Dstillery where she designed, developed, analyzed, and optimized machine learning that drives digital advertising. She started her career in Data... Read More →


Tuesday October 16, 2018 4:00pm - 4:40pm
Horace Mann

Attendees (54)