I have one conceptual question.
In Unsupervised Learning, when I have no labels. The anomaly detection model (Isolation forests, Autoencoders, Distance-based methods etc.), it should fit on a training data and then test( Train- Test split) just like a common supervised technique of creating the datafolds?
It helps in many ways during supervised learning to reduce the overfitting.
Or, it doesn't matter in unsupervised learning and I can train on all of my available dataset? Since there are no labels or measures to check the accuracy of fit.