Evaluate, Analyze, and Model Performance
About this course
In real-world machine learning work, building a model is only half the job. Knowing how to evaluate it, explain its weaknesses, and defend improvements is what makes your work trustworthy. In this course, you will learn how to evaluate regression and classification models using the right metrics, diagnose where models systematically fail, and determine whether performance differences actually matter. You will practice selecting RMSE and MAE for reporting housing-price models, analyzing confusion matrices to uncover false-positive patterns in spam filters, and using bootstrapping to test whether AUC improvements are statistically significant. Through short videos, guided coaching conversations, hands-on activities, and an ungraded lab, you will build confidence in interpreting model performance the way it is done on real teams. By the end of the course, you will be able to justify your evaluation choices and make evidence-based model decisions.
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What you'll learn
- evaluate regression and classification models
- diagnose model failures
- determine significance of performance differences
- select appropriate metrics for model reporting
- analyze confusion matrices
- test statistical significance of improvements using bootstrapping
Course objectives
- build confidence in interpreting model performance
- justify evaluation choices
- make evidence-based decisions regarding model development
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