Using a Stacking Model Ensemble Approach to Predict Rare Events | SciPy 2019 | Susan Yuhou Xia

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In this talk we will discuss a common and highly effective model ensemble technique known as stacking and how it can be used for classification to predict rare target events. We will start with the business problem, predicting which users will respond to online advertising and creating a list of these users called an “audience” to be used in ad serving. We will then describe stacking and explain the advantages, from reducing generalization bias to the practical implications of parallelization of model development amongst developers. Finally we will describe how we optimized a stacked model ensemble to create audiences.
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Hi Susan. Thanks for the talk. Very educational for me. I would like to ask if you are able to collect all the data of the users (ie all the consumers), you are actually getting the full set of data ? ie your sample is the actual data. Would overfitting be a problem ? The more you overfit, the closer you are to the actual. Thanks very much.

eatbreathedatascience