Model Evaluation Metrics
Learn how to perform a detailed confusion matrix analysis and calculate and plot ROC AUC curves.
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Evaluating model performance goes far beyond simple accuracy, especially in sensitive domains like healthcare. In this lesson, we’ll learn how to perform a detailed confusion matrix analysis and use ROC and precision-recall curves to better understand a model’s strengths and weaknesses, helping you make smarter, threshold-aware decisions. Let’s get started.
Confusion matrix analysis
For a medical diagnostic test predicting a rare disease, perform a comprehensive confusion matrix analysis for binary classification. What metrics can be derived from the confusion matrix, and how do they provide insights into the model’s performance beyond basic accuracy? Discuss the importance of each metric in the context of the given problem.
Sample answer
For this approach, we’ll take a look at the key metrics that can be calculated from a confusion matrix:
Accuracy
Precision ...