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I have 6 classes in my dataset and model. Dataset is regarding ECG signal

Having x number of records for each of these classes.

The confusion matrix looks like this - enter image description here

My question is, should I consider "Unreadable" records ( records which are not processed by model" while calculating Specificity, Sensitivity and positive predictivity.?

Any scientific evidence or paper link which consider "Unreadable" or noisy records in accuracy analaysis will help a lot.

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These metrics are used to assess the performance of your model. If certain observations are not used by the model it would therefore not make sense to include them when calculating the model's metrics. You should therefore not take these type of records into account and only look at records that are processed by your model.

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