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How to get confusion matrix when carrying out classification cross-validation

Hello,

I have been trying to get my confusion matrix from when carrying out K means cross validation for my data set but it has been abortive. Can anyone help?
I have attached my process below.
Thank you.

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AI Studio

Classification

Cross Validation

Accepted answers

MartinLiebig

Hi,

try to turn off the parallelization of cross validation. Then you can see the last iteration's value.

Best,

Martin

gwin

Hello,

It worked!

Thanks a lot for this.

Best Regards,

Godwin

All comments

MartinLiebig

Hi,

kmeans is clustering, meaning its an unsupervised method. How do you define a confusion matrix here?

Best,

Martin

gwin

hello,

So sorry I mixed things up, what I was doing was classification matrix (I later did K-Means but not for this process). But my question is why I could my cross-validation tool (during classification) not carry out my test and training set(at the same time) and bring out a confusion matrix table. When I tried looking for the performance vector (in my testing panel) from the performance tool, it seem not to bring out the performance vector option so that I can see the confusion matrix. (I hope this is clear).

I have attached the process again.

Best Regards,

Godwin

classification-performance.rmp

MartinLiebig

Hi,

maybe just because you did not connect the per port of x-validation to the right hand side?

Cheers,

Martin

gwin

Hello,

Thanks for the speedy response.
And yes that works for me if I want to get only the confusion matrix but then my professor's version can get the matrix even when he connects mod (instead of per) to res (and then right-click the performance inside the cross-validation to see the performance vector but I can't see it in mine when I follow the same steps).

I hope it is clear.

Godwin

MartinLiebig

Hi,

try to turn off the parallelization of cross validation. Then you can see the last iteration's value.

Best,

Martin

gwin

Hello,

It worked!

Thanks a lot for this.

Best Regards,

Godwin