imbalanced-learn and data-imbalance
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 data-imbalance
thecocolab/data-imbalance
Evaluating the effect of data balance on different classification metrics
This tool helps neuroscientists and researchers working with brain data (EEG/MEG) understand how class imbalance in their datasets affects machine learning classification results. You input your brain data and classification labels, and the tool evaluates different machine learning models and metrics. It then shows you which metrics and classifiers are most reliable when your data has uneven group sizes, helping you avoid misleading interpretations of your findings.
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