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 California Housing 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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