Something my lecturer said but I can't find why this is the case. I have to make a continuous variable into a categorical one, and the data is left skewed. Is it more important to have equal ranges or equal amounts of data in each range? Why?
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8Why do this any way? – Nick Cox Apr 13 '18 at 11:07
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Possible duplicate of What is the benefit of breaking up a continuous predictor variable? – kjetil b halvorsen Apr 13 '18 at 12:47
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1There is much good advice in the thread identified as duplicate, but it isn't what the OP is asking at all. A duplicate isn't ever the different question you should be asking. – Nick Cox Apr 13 '18 at 13:00
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My answer is - it depends.
It depends on why you do the discretization of the variable. If you want subsequently to build a statistical classifier from the discretized variable(s), you need to choose a binning (meaning the set of thresholds defining the interval bins all together), which is optimal for discerning the categories. If your purpose is merely display of the variable distribution in a histogram, you often prefer a uniform binning where the width of each bin is equal all the other bins.
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