Summary

Throughout this chapter, we looked into the differences between linear and logistic regression models. In addition, we reviewed when to choose linear or logistic models along with the trainers ML.NET provides. We also created and trained our first linear regression application using SDCA and ML.NET to predict employee attrition. We also created a logistic regression application using SDCA and ML.NET to provide file classification. Lastly, we also dove into how to evaluate a regression model and the various properties that ML.NET exposes to achieve a proper evaluation of your regression models.

In the next chapter, we will deep dive into binary classification algorithms.