How do you know if your model is going to work? Part 4: Cross-validation techniques

by John Mount (more articles) and Nina Zumel (more articles). In this article we conclude our four part series on basic model testing. When fitting and selecting models in a data science project, how do you know that your final model is good? And how sure are you that it's better than the models that you rejected? In this concluding Part 4 of our four part mini-series "How do you know if your model is going to work?" we demonstrate cross-validation techniques. Previously we worked on: Part 1: The problem Part 2: In-training set measures Part 3: Out of sample...

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