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Unbalanced Data Classification Techniques by samuelkmacho is a document available to read on EtoBox.
What is Unbalanced Data Classification Techniques about?
The document discusses the challenges of training and assessing classification models with unbalanced data, particularly in binary classification tasks where one class is rare. It highlights the detrimental effects of class imbalance on model learning and accuracy evaluation, proposing a unified framework using a smoothed bootstrap re-sampling technique called ROSE to address these issues. The authors emphasize the need for a systematic approach to improve both model estimation and accuracy assessment in th
- Author
- samuelkmacho
- Language
- EN