SMOTE (Synthetic Minority Oversampling Technique) for Handling Imbalanced Datasets
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SMOTE (Synthetic Minority Oversampling Technique) for Handling Imbalanced Datasets
Whenever we do classification in ML, we often assume that target label is evenly distributed in our dataset. This helps the training algorithm to learn the features as we have enough examples for all the different cases. For example, in learning a spam filter, we should have good amount of data which corresponds to emails which are spam and non ...
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