I don't know how to denormalize (0-1 normalized) data after prediction. I have 1 output and several input values. It's clear for 1 input I must use min and max value previously used for normalization.

But what should I do if for normalizing, each input was normalized separately, using its own min and max value?

Does anyone have any ideas?


2 Answers 2


You have n inputs and 1 output. What you need is comparing the predicted results $\hat y$ (a vector as long as the number of your test rows) with the real results $y$ (a vector as long as $\hat y$). Originally you had normalized the original data set using the min-max normalization through $\min Y$ and $\max Y$ (the min and max numbers assumed by the data output).

In order to evaluate your model you need to denormalize only the outputs. Since $\hat y_\text{norm}$ is the normalized test output you can do:

$$ \hat y = \hat y_\text{norm} \times (\max Y - \min Y) + \min Y $$

Then you'll compare $\hat y$ with $y$.

  • 1
    $\begingroup$ I edited your answer. This site supports $\TeX$ formatting for equations (see e.g. en.wikibooks.org/wiki/LaTeX/Mathematics) and this is a preferred formatting due to readability. $\endgroup$
    – Tim
    Commented Nov 24, 2015 at 9:20

You could run your prediction again, with normalisation and without normalisation and compare the output?

  • $\begingroup$ My prediction doesn't work without normalisation. However, I'm understand that I need use normalized Y for denormalize predicted.Y $\endgroup$
    – luckyi
    Commented Aug 9, 2013 at 11:37

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

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