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I am working on a classification problem with a highly imbalanced dataset. The ratio background to signal is about 20.

I trained an xgboost model. The ROC curve looks perfect and ROC_auc is also almost perfect 0.99. But the BDT response or probability to be[ a signal for a signal (training and test sets) looks very incorrectly.

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I tried to balance the data by adding weight to the data, but without success. Can you give me any advices how to deal with highly imbalanced datasets?

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  • $\begingroup$ What is the cost function you train the classifier with? How do you pick the threshold for the actual classification? What is BDT? $\endgroup$ – usεr11852 Apr 28 '17 at 20:43
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Learning under imbalanced datasets is an active research area in machine learning [1]. Algorithms that have to deal with imbalanced datasets can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods.

imbalanced-learn is a Python library that implements some of the aforementioned algorithms, has a simple interface and it is compatible to scikit-learn.

[1] http://ieeexplore.ieee.org/document/5128907/

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  • $\begingroup$ This is being automatically flagged as low quality, probably because it is so short. At present it is more of a comment than an answer by our standards. Can you expand on it? We can also turn it into a comment. $\endgroup$ – gung Dec 20 '16 at 19:07
  • $\begingroup$ @gung I extended my answer $\endgroup$ – chkoar Dec 23 '16 at 7:46

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