Expert Systems
Model Evaluation
A metric that measures the average difference between predicted probabilities and actual observed frequencies.
Detailed Explanation
Expected Calibration Error (ECE) quantifies how well a model's predicted probabilities align with actual outcomes. It averages the absolute differences between predicted confidence levels and observed frequencies across all prediction bins, providing a comprehensive measure of the model's probability calibration. Lower ECE indicates better calibration, meaning the predicted probabilities accurately reflect true likelihoods.
Use Cases
- •Use case: Evaluating a weather model’s confidence levels to improve forecast reliability and calibration accuracy.
Related Terms
Other terms in the Model Evaluation category