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As discussed here, In most cases, bin a continuous variable to discrete one is a bad idea. But when it will be good?

May attempt to answer:

  • Can I think binning will increase the variance of the model? As suggested in this post? Should we bin continuous variables?

  • If a variable has some extreme outliers or lots noise (say, income, there are persons make billions, and some data points are incorrect collected and shown as 0), binning might be better?

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Haitao Du
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  • *none linear* relationships? :) – Richard Hardy Nov 14 '16 at 13:50
  • See [What is the benefit of breaking up a continuous predictor variable?](http://stats.stackexchange.com/q/68834/17230). A brief riposte to your 2nd point is here: http://stats.stackexchange.com/questions/68834/what-is-the-benefit-of-breaking-up-a-continuous-predictor-variable/68839#comment228826_117994 – Scortchi - Reinstate Monica Nov 14 '16 at 14:17
  • Didn't you ask this before: http://stats.stackexchange.com/questions/230750/when-should-we-discretize-binning-continuous-independent-variables-features-an/230783#230783 ? – Matthew Drury Nov 14 '16 at 15:00
  • @MatthewDrury wow, it is surprising I do not remember my own questions. Worked overtime too much recently. – Haitao Du Nov 14 '16 at 15:01
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    Heh. Occasionally I find an answer and think "What, that's so wrong!" and then I scroll down, and it's me from a year ago. – Matthew Drury Nov 14 '16 at 15:02
  • @MatthewDrury but this question is good that Scortchi provided some links I did not found. Also, It remains me to study your answer and code carefully and make notes ! – Haitao Du Nov 14 '16 at 15:05
  • Eep. That code is, not so good. – Matthew Drury Nov 14 '16 at 16:46
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    Binning, taken to an extreme, puts all data into a single category--which obviously has no variance. Therefore binning *decreases* variance; it does not increase it. However, if you are referring to the variance of the *error* in a regression model, then binning a regressor (assuming the bins continue to be treated numerically rather than as factors) typically will *increase* the error variance because it destroys the (presumed) linear relationship between regressor and response, introducing "noise." – whuber Nov 14 '16 at 17:16

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