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Importance Weighted Cross Validation by jijinhan12 is a document available to read on EtoBox.
The paper introduces a novel method called Importance Weighted Cross Validation (IWCV) to address the issue of covariate shift in supervised learning, where training and test data come from different distributions. Traditional model selection techniques like cross validation fail under this scenario, but IWCV maintains unbiasedness, allowing for effective classification even with covariate shift. The method
- Author
- jijinhan12
- Language
- EN