UNK in ludwig learnning model I am using ludwig to train and test on the dataset. There is one independent variable that has a text data type. I used 'Parallel_CNN' as a text encoder for the independent variable. My dependent variable has the category data type and it has a binary value (i.e. 0 or 1). After the model finished I plotted the predicted and actual output in the form of confusion matrix which is shown as below:

I was expecting TP,TN,FP, and FN in the confusion matrix. However,  which is unknown also found in the confusion matrix. I tried to google it to see the meaning and impact of unknown on model learning but could not find any useful information. Could anyone help me why does 'Unk' comes into model? Does that mean model is not efficient in learning?
I don't know ludwig or even &quot;parallel CNN&quot; but there's a clear problem: this is not a confusion matrix, this is a heat map.

A confusion matrix shows the number of instances for every combination of predicted/true label. It's for categorical labels, you could indeed obtain TP/FP/FN/TN classification status from it.
A heatmap is for numerical values, as can be seen from the scale on the right side.

It's very likely that this heatmap is made of the probabilities of the labels for every instance, rather than the labels themselves (also the choice of colours is unusual but this doesn't matter).
The good news is that it appears that most instances which have 0 or 1 as true label are correctly predicted (high value for 1,1 and 0,0).
The bad news is that there's an issue in the labels themselves: apparently a large number of instances (at least a third I think) don't have any label and are considered 'UNKNOWN'. And apparently the system just considers these 'UNKNOWN' instances as a third class, so your labels are not binary actually. It manages to recognize this class quite well, but it's probably an issue you should investigate.