Train A Regression Model

Lesson 9 of 17

In the previous lesson, we built an F# application that sets up an ML.NET pipeline to load the New York TLC dataset and clean up the data using several feature engineering techniques.

So all we need to do is append a few command to the end of the pipeline to train and evaluate a regression model on the data.

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