# How can I measure the reliability of the specificity of a model with very small train, test, and validation datasets?

Stats newbie here. I have a small dataset of 646 samples that I've trained a reasonably performant model on (~99% test and val accuracy). To complicate things a little bit, the classes are somewhat unbalanced. It's a binary classification problem.

Here is my confusion matrix on training data

[[387   1]
[  1  73]]


on testing data:

[[74  1]
[ 0 10]]


on validation data:

[[85  1]
[ 0 13]]

1. Training Specificity: .986
2. Testing Specificity: .909
3. Validation Specificity: .928

My thoughts are that testing and validation have a very low specificity while training has a comparatively high specificity. However, given that only one sample is missed in both the testing and validation datasets, what is my real-world specificity? Is there a better generalizability measure? Is there something akin to a p-value that relates the reliability of the specificity given the size of the negative sample class?

Thanks!