This recording is from the 2022 RPM Virtual Seminar.

In traditional GLM modeling, categorical features are often treated with one-hot encoding. However, this method of encoding removes any ordering information inherent in the data. With accurate GLM, we propose to utilize an alternative encoding approach which allows us to preserve the ordering information. In this presentation, we will introduce the accurate GLM approach to modeling and discuss what motivated the approach. We will make a comparison of this approach to traditional GLM and GAM approaches, and illustrate the approach through simulated data modeling exercises and a case study.

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