I've been getting inconsistent results with a binary classification problem I'm trying to solve using a linear classifier and a custom feature extraction pipeline, and decided to do a quick check of my code for bugs by training and testing my classifier on the same dataset. I expected this to yield a very high (100%?) accuracy/recall and precision stats, but to my surprise, I got results comparable to or even lower than the ones I normally get on distinct training and testing sets (~70% recall).
Should a classifier be very accurate when applied to its own training data, or do I just have a bug in my code? I'm not very experienced in ML so any help at all would be greatly appreciated! Thanks!!