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2 cards from this section. Quick, good the night before a test.
Why should we not rely solely on accuracy for model evaluation?
Think about what happens if the majority class dominates the predictions.
Explain how precision and recall differ in evaluating a classification model.
Consider scenarios like spam detection or disease diagnosis.
What is a major risk of relying on accuracy alone?
Consider what accuracy measures and what contexts it fails.
True or False: Precision is not important in medical diagnosis.
Think about how critical negative outcomes are in this field.
You have a classification model that predicts customer churn. The confusion matrix shows 80 true positives, 10 false positives, 15 false negatives, and 45 true negatives. Evaluate the model using accuracy, precision, recall, and F1 score. Discuss the implications.
Check how variations in TP, FP, and FN affect these metrics.
Analyze a scenario where a fraud detection model has low recall but high precision. What could be the business implications of these metrics?
Think about costs associated with false negatives.
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