In the context of model evaluation, what does 'calibration' refer to?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — a is correct because calibration measures whether predicted probabilities are reliable — if a model predicts 70% for 100 events, approximately 70 should actually occur. B describes discrimination (AUC).
Full explanation below image
Full Explanation
A is correct because calibration measures whether predicted probabilities are reliable — if a model predicts 70% for 100 events, approximately 70 should actually occur. B describes discrimination (AUC). C describes hyperparameter optimization. D describes feature normalization, not calibration.