Hello,
To create a summary table of metrics (slope, intercept, Brier score, AUC) for multiple trained models in MATLAB, you can manually extract these values using model-specific properties and evaluation functions.
In MATLAB, you can construct it by combining outputs from functions like "coefCI" (for linear models), "predict", "loss", and "perfcurve" (for AUC).
Please refer to the attached documentations for more information.
- Confidence intervals of coefficient estimates of linear regression model - https://www.mathworks.com/help/stats/linearmodel.coefci.html
- Compute deep learning network output for inference - https://www.mathworks.com/help/deeplearning/ref/dlnetwork.predict.html
- Regression loss for linear regression models - https://www.mathworks.com/help/stats/regressionlinear.loss.html
- Receiver operating characteristic (ROC) curve or other performance curve for classifier output - https://www.mathworks.com/help/stats/perfcurve.html
I hope this helps.
