How can I measure the reliability of the specificity of a model with very small train, test, and validation datasets?
Real world data is "test dataset", right?
Data has to be divided in such a way that train-validation see part of data more than once while test data will be seen only once. In that sense, if the model is robust enough, it will perform well even on the test dataset. The assumption is that test data is as close as possible to real-world data.