Why doesn't class weight resolve the imbalanced classification problem?












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I know that in imbalanced classification, the classifier tends to predict all the test labels as larger class label, but if we use class weight in loss function, it would be reasonable to expect the problem to be solved. So why we need some approaches like down sampling or up sampling for imbalanced classification problem?









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    $begingroup$


    I know that in imbalanced classification, the classifier tends to predict all the test labels as larger class label, but if we use class weight in loss function, it would be reasonable to expect the problem to be solved. So why we need some approaches like down sampling or up sampling for imbalanced classification problem?









    share









    $endgroup$















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      0





      $begingroup$


      I know that in imbalanced classification, the classifier tends to predict all the test labels as larger class label, but if we use class weight in loss function, it would be reasonable to expect the problem to be solved. So why we need some approaches like down sampling or up sampling for imbalanced classification problem?









      share









      $endgroup$




      I know that in imbalanced classification, the classifier tends to predict all the test labels as larger class label, but if we use class weight in loss function, it would be reasonable to expect the problem to be solved. So why we need some approaches like down sampling or up sampling for imbalanced classification problem?







      classification unbalanced-classes weighted-data





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      asked 1 min ago









      user137927user137927

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