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    “Overfitting” is traditionally defined as training some flexible representation so that it memorizes the data but fails to predict well in the future. For this post, I will define overfitting more generally as over-representing the performance of systems.
     There are two styles of general overfitting: overrepresenting performance on particular datasets and (implicitly) overrepresenting performance of a method on future datasets.We should all be aware of these methods, avoid them where possible, and take them into account otherwise. I have used “reproblem” and “old datasets”, and may have participated in “overfitting by review”—some of these are very difficult to avoid.

    See the link for complete Table

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    • 2015-01-11 10:04:44Z
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    zagros said 2015-01-11 22:20:51Z
    I like your post. that would be good if you could tag keywords as well while you are posting them, this type of meta data could be helpful for searching in ... Read moreour future release.
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