Join us Tuesday, February 28th at 6:30pm to hear from Claudia Perlich,Chief Data Scientist at Dstillery. She will discuss "Predictability and other Predicaments in Machine Learning Applications" -- and has provided the following description of her talk:

In the context of building predictive models, predictability is usually considered a blessing. After all that is the goal...to 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. In the worst case, predictive models can introduce biases NOT even present in the training data. 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 a significantly negative impact on the usefulness and ethics of predictive modeling.

Event Schedule:
6:30 - 7:00 - Guests arrive, enjoy food & drinks
7:00 - 7:30 - Claudia presents
7:30 - 8:00 - Q&A + networking

Claudia Perlich is the Chief Data Scientist at Dstillery. Prior to joining Dstillery (formerly at Media6Degrees), she spent five years working at the Data Analytics Research group at theIBMT.J. Watson Research Center, concentrating on research in data analytics and machine learning for complex real-world domains and applications. She has been published in over 30 scientific publications and holds multiple patents in the area of machine learning. Claudia has won many data mining competitions, including the prestigious 2007KDDCUPon movie ratings, the 2008KDDCUPon breast-cancer detection, and the 2009KDDCUPon churn and propensity predictions for telecommunication customers. Claudia received her Ph.D. in Information Systems from Stern School of Business, New York University in 2005, and holds a Master of Computer Science from Colorado University.

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