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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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MAY 20, 2021

This carries the risk of this modification performing worse than simpler approaches like majority under-sampling. note that this variant “performs worse than plain under-sampling based on AUC” when tested on the Adult dataset (Dua & Graff, 2017). Using the adap learning algorithm to forecast the onset of diabetes mellitus.

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ML internals: Synthetic Minority Oversampling (SMOTE) Technique

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