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Your model is probably overfitted now because you are adding too much synthetic data probably "repeating" some. For this kind of dataset SMOTE is not enough, remember the techniques you have for fighting imbalancement, usually one needs to apply many of them. You perform this pipeline: 1.- Apply Tomek links to remove as much as you can from the ...


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From your confusion matrix, your model only predict Benign class. It seems that you have a degenerate model. It means that there is a problem somewhere, either your model hasn't learned at all or it has learned too well based on a poorly chosen metric. This doens't really appears to be linked to SMOTE, but seems to be about your calibration process. We can't ...


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When you have an imbalanced class in a classification problem–the model tends to disregard the lower class sample. This is a problem because the models accuracy can be high but the errors for a single class would be very high. A good metric to look at would be the f1-score. To fix an issue like this, you need to use SMOTE (synthetic minority oversampling ...


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Is a lot there to explain of “why” when u basically do not provide the code or what u did there. Also based on your CM I assume your model is doing just what is trained to do, u do get good performance of the model u fit, letting aside that minority classes are ignored. Based on my experience I would use sample size weight for the models instead of creating ...


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If the dataset has imbalance class (i.e. class 1 has 90% and class 0 has 10%) try to add some techniques like Up sampling or Down sampling in pre-processing stage to predict the sophisticated customer selection for that particular deal.


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Here is my implementation, I hope it can help you. This is for image segmentation problem (binary classification). The ground truth image size is (512,512,1): def weighted_BCE_loss(y_true, y_pred, positive_weight=5): # y_true: (None,None,None,None) y_pred: (None,512,512,1) y_pred = K.clip(y_pred, min_value=1e-12, max_value=1 - 1e-12) weights =...


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First of all, resampling or artificial sampling is not necessary nor panacea in many cases of imbalanced learning. Other methods may yield good results (eg threshold tuning, class weights adjustment and so on..). That being said, resampling is indeed helpful in many cases of imbalanced learning. But when do we have imbalance really? Is a 60-40 split ...


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It is important to understand that your precision and recall are associated with a binary decision threshold. Basically, the outputs of the model are converted to a binary decision using this threshold. There are usually default threshold selection process implemented in standard libraries, but they don't really work for imbalanced cases. One option is to ...


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