Create confusion matrix from LDA model
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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.
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the cyclist
2024-2-23
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The ClassificationDiscrimant class has a predict function. You can input the predicted and actual labels into the confusionchart function.
4 个评论
the cyclist
2024-2-23
Yes, cross-validation can affect how you handle the confusion matrix. And, in some ways you cannot have both the "easiest" and "best" way to do this.
I have typically used ensemble models (with cross-validation), and in the end use the selected "best" model, and its prediction. That is perhaps the easiest approach.
But, each fold actually has its own prediction. So, you could calculate a confusion matrix for each fold. Inspecting multiple confusion matrices can actually be a good way to get a feel for how much uncertainty (due, e.g. to sampling error) you have in that estimate. This is actually true of all the statistics that come out of a machine learning model, such as the accuracy, F1 score, etc.
So, in the end, you are left with how to "average" those statistics over the folds. It's all a bit tricky, and I am not sure if there is a canonical or even a conventional way to do that.
the cyclist
2024-2-23
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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