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There are several methods to normalize data, among them are:

min-max, z-score and scale decimal.

Can I use any one or with what criteria should I choose one of them?

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No specific answer to your question, it all depends on which algorithm you are using or in other words how you will use the normalized data. Based on my experience I found that the zscore normalization performs the best, especially if you are using svm or nn.

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  • $\begingroup$ Any article that refutes your choice? What is the advantage of z-score? $\endgroup$ – x-rw Sep 24 '18 at 5:36
  • $\begingroup$ If you have a strong math background / if you are a master/PhD student you can look at this ftp.stat.math.ethz.ch/Doc/Neural/FAQ2.html. this is one of the most important articles that explain why data normalization is important. If you do not have a strong math you can search the Google for simple articles that simply explain why data normalization is important and why zscore is very successful. If you do not find this helpful, I can share another article with you, just let me know $\endgroup$ – Bashar Haddad Sep 24 '18 at 5:48
  • $\begingroup$ ftp.stat.math.ethz.ch/Doc/Neural/FAQ2.html#A_std $\endgroup$ – Bashar Haddad Sep 24 '18 at 5:50
  • $\begingroup$ I understand the importance of normalization, what I do not understand is the choice of z-score in relation to the other methods. $\endgroup$ – x-rw Sep 24 '18 at 6:07
  • $\begingroup$ This is what the article explains, zscore normalizes the data to be between -1 and +1, with 1 standard deviation. This is very beneficial to many training algorithms when optimize the parameters, including the nn and svm. $\endgroup$ – Bashar Haddad Sep 24 '18 at 6:10

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