Create confusion matrix from LDA model

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Leon
Leon on 23 Feb 2024
Edited: Leon on 26 Feb 2024
It is easy to train an LDA model and find its accuracy by cross-validation as below:
Mdl = fitcdiscr(data, "Response_var_name", CrossVal="on");
validationAccuracy = 1 - kfoldLoss(Mdl, 'LossFun', 'ClassifError');
However, what is the easiest/best way to get the confusion matrix?
Thanks.

Accepted Answer

the cyclist
the cyclist on 23 Feb 2024
The ClassificationDiscrimant class has a predict function. You can input the predicted and actual labels into the confusionchart function.
  4 Comments
Leon
Leon on 23 Feb 2024
Edited: Leon on 26 Feb 2024
Good to know. Maybe I would be better to use kfoldPredict(), then?
the cyclist
the cyclist on 23 Feb 2024
Yes, I think that is sensible.
I have to admit, though, that I don't fully comprehend how kfoldPredict goes from this statement (from the documentation)
========================================================================
"For every fold, kfoldPredict predicts class labels for validation-fold observations using a classifier trained on training-fold observations."
========================================================================
-- to a single prediction for the model (as opposed to a prediction per fold, which is how I read that statement). It is presumably possible to use the debugger to step into the function and see exactly what it is doing, but I have not done that.

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