Train A Regression Model

Lesson 1 of 8

We’re going to continue with the code we wrote in the previous lab. That F# application set up an ML.NET pipeline to load the California Housing dataset and clean up the data using several feature engineering techniques. 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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