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[D] Handling noisy labels in large datasets with slight imbalance

Hey all, I have large binary classification dataset with follow counts 0: 200k 1: 500k

Now the labels are not true labels. I know the percentage of correct of each label group. By this I mean I know that 80% of 0 are correct and 85% of 1 are correct, I don’t know which.

Now I have tried the following:- ° Random first with class weight – massively overfit and if played around with max _depth parameter to reduce overfitting however I am unable to get good results. ° Tried oversampling like SMOTE etc but they take large amount of time.

Do you have any suggestions how to deal with imbalance and noisy labels?

submitted by /u/amil123123
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.