Skip to main content

Resampling strategies for regression

Project description

Resreg is a Python package for resampling imbalanced distributions in regression problems.

If you find resreg useful, please cite the following article:

  • Gado, J.E., Beckham, G.T., and Payne, C.M (2020). Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning. J. Chem. Inf. Model. 60(8), 4098-4107.

If you use RO, RU, SMOTER, GN, or WERCS methods, also cite

  • Branco, P., Torgo, L., and Ribeiro, R.P. (2019). Pre-processing approaches for imbalanced distributions in regression. Neurocomputing. 343, 76-99.

If you use REBAGG, also cite

  • Branco, P., Torgo, L., and Ribeiro, R.P. (2018). REBAGG: Resampled bagging for imbalanced regression. In 2nd International Workshop on Learning with Imbalanced Domains: Theory and Applications. pp 67-81.

If you use precision, recall, or F1-score for regression, also cite

  • Torgo, L. and Ribeira, R.P. (2009). Precision and recall for regression. In International Conference on Discovery Science. pp332-346


Preferrably, install from GitHub source. The use of a virtual environment is strongly advised.

git clone
cd resreg
pip install -r requirements.txt
python install

Or, install with pip (less preferred)

pip install resreg


  1. Python 3
  2. Numpy
  3. Scipy
  4. Pandas
  5. Scikit-learn


A regression dataset (X, y) can be resampled to mitigate the imbalance in the distribution with any of six strategies: random oversampling, random undersampling, SMOTER, Gaussian noise, WERCS, or Rebagg.

  1. Random oversampling (RO): randomly oversample rare values selected by the user via a relevance function.
  2. Random undersampling (RU): randomly undersample abundant values.
  3. SMOTER: randomly undersample abundant values; oversample rare values by interpolation between nearest neighbors.
  4. Gaussian noise (GN): randomly undersample abundant values; oversample rare values by adding Gaussian noise.
  5. WERCS: resample the dataset by selecting instances using user-specified relevance values as weights.
  6. REBAGG: Train an ensemble of Scikit-learn base learners on independently resampled bootstrap subsets of the dataset.

See the tutorial for more details.


import resreg
from sklearn.metrics import train_test_split
from sklearn.metrics import RandomForestRegressor

# Split dataset to training and testing sets
X_train, X_test, y_train, y_test = resreg.train_test_split(X, y, test_size=0.25)

# Resample training set with random oversampling
relevance = resreg.sigmoid_relevance(y, cl=None, ch=np.percentile(y, 90))
X_train, y_train = resreg.random_oversampling(X_train, y_train, relevance, relevance_threshold=0.5,

# Fit regressor to resampled training set
reg = RandomForestRegressor(), y_train)
y_pred = reg.predict(X_train, y_train)

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for resreg, version 0.2
Filename, size File type Python version Upload date Hashes
Filename, size resreg-0.2-py3-none-any.whl (27.4 kB) File type Wheel Python version py3 Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page