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Bechir Jamoussi
Bechir Jamoussi

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Feature Engineer each class separately in Binary Classification

I have an imbalanced tabular dataset, my problem is a binary classification. The dataset is strongly imbalanced so I have performed oversampling, but it did not solve the issue, you can find the Classification Report below:(The accuracy is 88% but I don't care, it does not represent well the performance since the dataset is imbalanced)


The only explanation that I found is that the features selected are well suited to detect "0" while they don't tell much information about the "1" class, is there any way to catch the best features that represent the "1" class, maybe split the dataset into 1_Class_Dataset and 0_Class_Dataset and catch the best features for each and then combine both? If it is not possible can you please suggest another solution?

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