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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Use this if you are a neuroscientist applying machine learning to brain imaging data and need to accurately assess model performance, especially when dealing with unequal numbers of samples across your experimental conditions or diagnostic groups.
Not ideal if you are working with non-neuroscience data or require multi-class classification beyond binary problems, as it's specifically tailored for neuroscience ML and currently only supports binary classification.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Jun 05, 2023
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