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The Imbalanced Classification Add-In: Compare Sampling Techniques and Models (2020-US-30MP-625)

Level: Advanced


Michael Crotty, JMP Senior Statistical Writer, SAS
Marie Gaudard, Statistical Consultant, Statistical Consultant
Colleen McKendry, JMP Technical Writer, JMP


The need to model data sets involving imbalanced binary response data arises in many situations. These data sets often require different handling than those where the binary response is more balanced. Approaches include comparing different models and sampling methods using various evaluation techniques. In this talk, we introduce the Imbalanced Binary Response add-in for JMP Pro that facilitates comparing a set of modeling techniques and sampling approaches.

The Imbalanced Classification add-in for JMP Pro enables you to quickly generate multiple sampling schemes and to fit a variety of models in JMP Pro. It also enables you to compare the various combinations of sampling methods and model fits on a test set using Precision-Recall ROC, and Gains curves, as well as other measures of model fit. The sampling methods range from relatively simple to complex methods, such as the synthetic minority oversampling technique (SMOTE), Tomek links, and a combination of the two. We discuss the sampling methods and demonstrate the use of the add-in during the talk.