imbalanced-learn and machine-learning-imbalanced-data
The first is a mature, production-ready resampling and algorithmic library for handling class imbalance, while the second is an educational repository teaching imbalance techniques using that library as a dependency—making them complementary rather than competitive.
About imbalanced-learn
scikit-learn-contrib/imbalanced-learn
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
This tool helps data scientists and machine learning engineers build more accurate predictive models when their datasets have unequal numbers of examples across different categories. It takes a raw, imbalanced dataset and processes it using various re-sampling techniques to create a more balanced dataset, which then leads to improved model performance, especially for the under-represented categories. This is particularly useful for tasks where correctly identifying rare events is critical.
About machine-learning-imbalanced-data
solegalli/machine-learning-imbalanced-data
Code repository for the online course Machine Learning with Imbalanced Data
When building machine learning models, especially for rare events like fraud detection or disease diagnosis, you often encounter imbalanced datasets where one outcome is far less common. This project helps you address this by providing techniques to balance your data, leading to more accurate and reliable predictions. Data scientists and machine learning engineers will find this useful for improving their model performance.
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