Oversampling methods for numerical data (regression)

Asked Oct 19 '19 at 12:43

Active Dec 06 '22 at 04:27

Viewed 2,943 times

There are many oversampling methods for categorical labels (for example SMOTE and Rose, etc.).

But, are there oversampling method for numerical labels (the thing that I want to predict with my features), in the sense that it applies something similar SMOTE or Rose for the label that is highly skewed? (I don't want to normalize or scale my label/class) for regression types of tasks?

asked Oct 19 '19 at 12:43

jennifer ruurs

Have you gone through the linear regression diagnostics in the normal way? http://www.sthda.com/english/articles/39-regression-model-diagnostics/161-linear-regression-assumptions-and-diagnostics-in-r-essentials/. You can transform the data if necessary e.g. log etc. – Christopher John Nov 02 '19 at 06:53
It is not clear that oversampling even helps us in the classification setting. – Dave Mar 23 '23 at 20:26

Oversampling methods for numerical data (regression)

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