Train A Regression Model

Lesson 9 of 17

We’re going to continue with the code we wrote in the previous lab. That C# application set 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.

...

Please provide your access code to unlock and read the remainder of this lesson. Your code will be securely stored in your browser, so you only need to enter it once. If you do not have an access code for this lab, you can get one here.

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
Previous Lesson Next Lesson