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<DIV><FONT face=Arial size=2>Hello,</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>The question looks like simple. It's probably even
stupid. But I spent several hours</FONT></DIV>
<DIV><FONT face=Arial size=2>searching Internet, downloaded tons of papers,
where deviance is mentioned and...</FONT></DIV>
<DIV><FONT face=Arial size=2>And haven't found an answer. </FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Well, it is clear for me the using of entropy when
I split some node of a classification tree.</FONT></DIV>
<DIV><FONT face=Arial size=2>The sense is clear, because entropy is an old good
measure of how uniform is distribution.</FONT></DIV>
<DIV><FONT face=Arial size=2>And we want, for sure, the distribution to be
uniform, represent one class only as the best.</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Where deviance come from at all? I look at a
formula and see that the only difference to</FONT></DIV>
<DIV><FONT face=Arial size=2>entropy is use of *number* of each class points,
instead of *probability* as a multiplier</FONT></DIV>
<DIV><FONT face=Arial size=2>of log(Pik). So, it looks like the deviance and
entropy differ by factor 1/N (or 2/N), where</FONT></DIV>
<DIV><FONT face=Arial size=2>N is total number of cases. Then WHY to say
"deviance"? Any historical reason?</FONT></DIV>
<DIV><FONT face=Arial size=2>Or most likely I do not understand something very
basic. Please, help.</FONT></DIV>
<DIV><FONT face=Arial size=2></FONT> </DIV>
<DIV><FONT face=Arial size=2>Thanks,</FONT></DIV>
<DIV><FONT face=Arial size=2>Alexander Skomorokhov,</FONT></DIV>
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