I generate input data for my model at different temperatures. At each temperature I generate 1000 samples and in each sample I have 16 features. So the shape of array is (1000,16) at each temperature. I want to normalize data between 0 and 1. But I am really confused that what is the strategy of normalization in machine learning language? Should I normalize feature of each single sample such that each sample is like [0,.....1] or should I normalize along column axis. Secondly I also want to know that should I perform this normalization separately for each temperature?
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$\begingroup$ You normalize each variable (column) separately. $\endgroup$– user2974951Commented Oct 25, 2018 at 8:00
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$\begingroup$ You mean feature of every single sample? $\endgroup$– herryCommented Oct 25, 2018 at 9:22
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$\begingroup$ You normalize each variable (feature) separately, for all samples. $\endgroup$– user2974951Commented Oct 25, 2018 at 9:26
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$\begingroup$ Sorry may be I'm misunderstanding your statement.In my question I have 16 features in each sample. So after normalization I will get [0......1].length of this vector is 16.Is this what you want to convey? $\endgroup$– herryCommented Oct 25, 2018 at 10:28
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$\begingroup$ Normalize the whole feature (with all the samples), that is extract every 1000 length vector (total of 16), apply normalization to each such vector. $\endgroup$– user2974951Commented Oct 25, 2018 at 10:32
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You should normalize each column separately. After normalization each feature should have a maximum value at 1 and a minimum at 0.
