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Dec 12, 2014 at 22:09 comment added gaborous @TomMinka in fact overfitting can be caused by complexity (a model too complex to fit a too simple data, thus additional parameters will fit whatever comes at hand) or, as you pointed, by noisy features that gets more weights in the decision than pertinent features. And there are a lot of other possible sources of overfitting (intrinsic variance of the data or model, data not pertinent to represent the target goal, etc.). I think we ought to say that there are overfittings, not just overfitting (which imply that there's just one cause, which often is not correct).
Dec 12, 2014 at 20:42 comment added Tom Minka A nice feature of this example is that it demonstrates the difference between overfitting and complexity. The rule "As goes California, so goes the nation" is simple, yet still overfit.
Dec 11, 2014 at 19:55 history edited dimitriy CC BY-SA 3.0
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Dec 11, 2014 at 18:05 history made wiki Post Made Community Wiki by whuber
Dec 11, 2014 at 8:40 comment added Neil Slater I think you should add something about sample bias to explain how this relates to overfitting. Just a cut&paste of the cartoon is missing the explanation.
Dec 11, 2014 at 7:49 history edited Stephan Kolassa CC BY-SA 3.0
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Dec 11, 2014 at 7:37 history answered dimitriy CC BY-SA 3.0