0
$\begingroup$

I implemented my ANN using SKlearn module's class MLPClassifier. Fitting it on some data and testing it on a very specific subset of said training data, it gives a score of 1.0, but actually using the ANN to generate outputs for said testing data almost always gives incorrect output. What could be the possible reasons for this? I am using this ANN as a memory network and there is never a case when input is out of training data.

$\endgroup$
2
  • 2
    $\begingroup$ Overfitting overfitting overfitting! $\endgroup$ – Tolga Birdal Apr 18 '17 at 20:17
  • $\begingroup$ My answer on a similar question might help you. $\endgroup$ – Kiritee Gak Apr 19 '17 at 3:38
3
$\begingroup$

You're testing your neural net with data from the training set, of course it will get you great results. But you're neural net is now trained for a specific data and is unable to make generalization. This is known as Overfitting.

You should either split your data into test/train or use another technique to estimate your NN performance. A good technique is K-fold Cross validation.

Also check Overfitting wiki page and Cross validation on sklearn if you want more details.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.