In the decision tree based classification technique. What is the difference among the different approaches like entropy, gini index? When to use entropy and when to use gini index?.
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1$\begingroup$ This question appears to be off-topic because it is about math. $\endgroup$– Sean OwenOct 2, 2013 at 19:07
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1$\begingroup$ I would use entropy if you're not sure. One advantage of entropy is that it is also defined for continuous variables, but not so for Gini impurity. $\endgroup$– Sean OwenOct 2, 2013 at 19:08
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2$\begingroup$ @SeanOwen, then again, everything is about math. $\endgroup$– Don RebaOct 2, 2013 at 19:40
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$\begingroup$ Sure, at some level, but at the level that StackExchange divides up, questions here aren't generally about math. math.stackexchange.com will get much better answers, although for some reason there's no option to close and move it there. $\endgroup$– Sean OwenOct 2, 2013 at 20:30
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Here is a quote from a paper on the subject:
Different split criteria were proposed in the literature (Information Gain, Gini Index, etc.). It is not obvious which of them will produce the best decision tree for a given data set. A large amount of empirical tests were conducted in order to answer this question. No conclusive results were found.
