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How to appropriately stratify an imbalanced dataset when splitting into practice and examination sets and when employing k-fold cross-validation.
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The way to use algorithms from the field of outlier detection and anomaly detection for imbalanced classification.
How to make use of a modified Edition of k-fold cross-validation and practice-take a look at splits that get the class distribution under consideration.