This report on evaluating machine learning models arose out of a sense of need. The content was first published as a series of six technical posts on the Dato Machine Learning Blog. I was the editor of the blog, and I needed something to publish for the next day. Dato builds machine learning tools that help users build intelligent data products. In our conversations with the community, we sometimes ran into a confusion in terminology. For example, people would ask for cross-validation as a feature, when what they really meant was hyperparameter tuning, a feature we already had. So I thought, “Aha! I’ll just quickly explain what these concepts mean and point folks to the relevant sections in the user guide.” Evaluating Machine Learning Models

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