What’s your approach to highly imbalanced data sets?

• ydata-synthetic

  60 1,286 7.6 Jupyter Notebook

  Synthetic data generators for tabular and time-series data

  

There's a pletora of undersampling and oversampling models you can try out. To avoid removing information form the dataset, you can focus on oversampling techniques. You can try imbalanced-learn or smote-variants. Given enough data, using fully synthetic data is also an option, you can check ydata-synthetic for it. Let us know how it turned out!

• general_class_balancer

  1 3 10.0 Python

  Data matching algorithm for categorical and continuous variables

Multivariate data matching. I wrote a function to do this in grad school: https://github.com/mleming/general_class_balancer

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• deodel

  13 5 6.3 Python

  A mixed attributes predictive algorithm implemented in Python.

Just to mention that there is also a new algorithm that is immune to the imbalance of data. An implementation in python is available at: - https://github.com/c4pub/deodel

• imbalanced-learn

  1 6,697 7.4 Python

  A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

  

There's a pletora of undersampling and oversampling models you can try out. To avoid removing information form the dataset, you can focus on oversampling techniques. You can try imbalanced-learn or smote-variants. Given enough data, using fully synthetic data is also an option, you can check ydata-synthetic for it. Let us know how it turned out!

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