evaluate-model-calibration

To check if predicted probabilities match observed outcomes, you evaluate model calibration using reliability diagrams, Expected Calibration Error (ECE), and Brier Score to quantify and visualize performance.

Calibration in AI evaluation measures whether predicted probabilities accurately reflect actual outcome frequencies, whereas discrimination measures the model's ability to separate different outcome classes regardless of probability accuracy.

You create a reliability diagram for a health AI model by using Python with numpy, scikit-learn, and matplotlib to plot predicted probabilities against observed frequencies, visually assessing calibration performance.

Yes, you can use Expected Calibration Error and Brier Score to assess clinical risk predictions by quantitatively measuring the accuracy of predicted probabilities against observed diagnostic and prognostic outcomes.

If your diagnostic AI model has poor calibration performance, you should apply recalibration strategies to improve predicted probability accuracy, ensuring reliable estimates for clinical decision-making and preventing misdiagnoses.

Yes, you need matplotlib and scikit-learn along with numpy to calculate Expected Calibration Error (ECE) and generate reliability diagrams, as these Python dependencies provide the required statistical and visualization functions.