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Create Artificial Data With SMOTE
How you can leverage a simple algorithm to compensate for lack of dataPhoto by Brett Jordan on Unsplash
Data imbalance is ubiquitous in machine learning. Real data rarely represents every class equally. In applications such as disease diagnosis, fraud detection, and spam classification, some classes will always be underrepresented.
This is a major obstacle for many machine learning related endeavors. After all, if you lack the data for a specific outcome, your model will not be able to predict …