How to train and evaluate a deep learning model for very imbalanced classification (e.g. classification with 99.82% of negatives and 0.18% of positives)? Here's an example using the Kaggle credit card fraud dataset.https://colab.research.google.com/drive/1xL2jSdY-MGlN60gGuSH_L30P7kxxwUfM …
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the other questions only come up 0.18% of the time
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Not sure if appears all the time, but here it goes: What are some tips to training DNN for regression tasks? Most work out there focus on classification
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