How does the mean absolute error related to the relative error from log-transformed labels?












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In machine learning we sometimes log-transform labels (or features). My understanding is that optimizing for the mean absolute (or squared) error using log-transformed labels optimizes for the relative difference between the untransformed labels.



Is this true? If so, is it possible to make a statement about the mean relative error between the untransformed labels given the mean absolute error on the transformed labels?










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  • $begingroup$
    Have a look at math.stackexchange.com/questions/3025085/…
    $endgroup$
    – Claude Leibovici
    Dec 6 '18 at 9:32
















0












$begingroup$


In machine learning we sometimes log-transform labels (or features). My understanding is that optimizing for the mean absolute (or squared) error using log-transformed labels optimizes for the relative difference between the untransformed labels.



Is this true? If so, is it possible to make a statement about the mean relative error between the untransformed labels given the mean absolute error on the transformed labels?










share|cite|improve this question









$endgroup$












  • $begingroup$
    Have a look at math.stackexchange.com/questions/3025085/…
    $endgroup$
    – Claude Leibovici
    Dec 6 '18 at 9:32














0












0








0





$begingroup$


In machine learning we sometimes log-transform labels (or features). My understanding is that optimizing for the mean absolute (or squared) error using log-transformed labels optimizes for the relative difference between the untransformed labels.



Is this true? If so, is it possible to make a statement about the mean relative error between the untransformed labels given the mean absolute error on the transformed labels?










share|cite|improve this question









$endgroup$




In machine learning we sometimes log-transform labels (or features). My understanding is that optimizing for the mean absolute (or squared) error using log-transformed labels optimizes for the relative difference between the untransformed labels.



Is this true? If so, is it possible to make a statement about the mean relative error between the untransformed labels given the mean absolute error on the transformed labels?







statistics machine-learning






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share|cite|improve this question











share|cite|improve this question




share|cite|improve this question










asked Dec 6 '18 at 9:30









tibbetibbe

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101












  • $begingroup$
    Have a look at math.stackexchange.com/questions/3025085/…
    $endgroup$
    – Claude Leibovici
    Dec 6 '18 at 9:32


















  • $begingroup$
    Have a look at math.stackexchange.com/questions/3025085/…
    $endgroup$
    – Claude Leibovici
    Dec 6 '18 at 9:32
















$begingroup$
Have a look at math.stackexchange.com/questions/3025085/…
$endgroup$
– Claude Leibovici
Dec 6 '18 at 9:32




$begingroup$
Have a look at math.stackexchange.com/questions/3025085/…
$endgroup$
– Claude Leibovici
Dec 6 '18 at 9:32










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