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Textbook:
Evaluating Machine Learning Models
A Beginner's Guide to Key Concepts and Pitfalls
Alice Zheng
This report focuses on model evaluation. It is for folks who are start‐ ing out with data science and applied machine learning. Some seas‐ oned practitioners may also benefit from the latter half of the report, which focuses on hyperparameter tuning and A/B testing. I certainly learned a lot from writing it, especially about how difficult it is to do A/B testing right. I hope it will help many others build measurably better machine learning models!
This report includes new text and illustrations not found in the orig‐ inal blog posts. In Chapter 1, Orientation, there is a clearer explana‐ tion of the landscape of offline versus online evaluations, with new diagrams to illustrate the concepts. In Chapter 2, Evaluation Met‐ rics, there’s a revised and clarified discussion of the statistical boot‐ strap. I added cautionary notes about the difference between train‐ ing objectives and validation metrics, interpreting metrics when the data is skewed (which always happens in the real world), and nested hyperparameter tuning. Lastly, I added pointers to various software.........
evaluating-machine-learning-models.pdf
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