Hello Aditya!



 kendreaditya:

I do agree that accuracy data does look fishy,
…
Any tips?


I won’t guarantee it, but the results you posted look sufficiently odd
to me that I think it’s likely that you have a bug somewhere.
So, debugging …
Check intermediate results.  Are they correct or do they at least make
sense?
See if you can simplify your code.  Sometimes in the process, you’ll
recognize a bug.  Or sometimes your simpler code will start working
the way you expect, and you can track down what change in the
simplification process caused things to work.
Review your code carefully, line by line.  Is the calculation you are
doing one that makes sense?  Are you actually doing the calculation
you think you are doing, or has a typo crept in somewhere?
Here is one specific consistency check I would suggest you make:
Since the oddness is in the comparison between your training and
test results, try running your test on a subset of your training data.
Instead of taking 33 test samples that are not part of your training
data, randomly select 33 of your 66 training samples and use them
for your test data.
But:  Do everything else in your calculation exactly the same way.
Don’t change your code to make a quick hack to get the “test results”
from your training data – process your 33 “fake” samples exactly the
same way you process your real ones – any kind of preprocessing,
etc.  Pass them through the exact same test-result evaluation code,
and so on.
Because you’re now running your “test” on training data, you shouldn’t
see any kind of overfitting.  If you do, you’ll know that there’s a bug
somewhere.
Good luck.
K. Frank