I am looking into upsampling an imbalanced dataset for a regression problem (Numerical target variables) in python.

I attached paper and R package that implement SMOTE for regression, can anyone recommend a similar package in Python? Otherwise, what other methods can be use to upsample the numerical target variable?

SMOTE for Regression

smoteRegress: SMOTE algorithm for imbalanced regression problems


I found the following python library which implements Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise


  • $\begingroup$ the imblearn package implements the smote oversampling method $\endgroup$
    – Victor Ng
    Commented Mar 3, 2020 at 21:47
  • $\begingroup$ @VictorNg but it does not allow continuous target variable. $\endgroup$ Commented Mar 3, 2020 at 22:53
  • $\begingroup$ oh right regression. i need to learn to read. would you lose too much information by binning the continuous variable into categories? $\endgroup$
    – Victor Ng
    Commented Mar 4, 2020 at 2:48
  • 1
    $\begingroup$ Yes, I will lose much because I am trying to optimize the MSE $\endgroup$ Commented Mar 4, 2020 at 15:09

1 Answer 1


I think SMOGN will work for your problem. The method is described in a paper titled: "SMOGN: a Pre-processing Approach for Imbalanced Regression". You can find it on arXiv. There is also a python implementation called "SMOGN" which can be installed through PyPI. You can find the package description at https://pypi.org/project/smogn/

  • $\begingroup$ Thanks! I found out about the same package a few weeks ago. $\endgroup$ Commented May 18, 2020 at 13:47

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