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Deep dive analysis of various oversampling techniques Image by LTD EHU from Pixabay Modeling imbalanced data is the major challenge that we face when we train a model. For dealing with the classification problems the class balance of the target class label plays an important role in modeling. For imbalance class problems i.e presence of minority class in the dataset, the models try to learn only the majority class and result in biased prediction.
Image by H Heyerlein on Unsplash Hands-on Tutorials Can you tell the difference between these images? Easy right? And I’m sure those of you familiar with classical machine learning methods could whip up a classification algorithm to automate this process in a heartbeat. But what about when we don’t have any labels? Hopefully, for some of you, some fancy unsupervised clustering algorithms come to mind.
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