# How do you do 1-vs-rest classifiers in XGBoost Library (Not Sklearn)?

I am working with a very large dataset that would benefit from using training continuation with the xgb_model parameter in xgb.train(). The label (Y) of dataset itself has 4 classes and is highly imbalanced, so I would like to generate per-label PR curves for it to evaluate its performance, and would thus need to treat each class as it's own binary problem using a one-vs-rest classifier. After a lot of reading I haven't found an equivalent to sklearn's OneVsRestClassifier in the xgboost library. Could anyone provide some guidance on how to implement continuously training one-vs-rest classifiers using the XGBoost library? Thank you in in advance.

• You can continue training in the sklearn API, see stackoverflow.com/q/66794560/10495893. However, it's not clear how to do that to each model inside a OneVsRestClassifier. My guess is you'd just need to do this manually, looping over target classes. – Ben Reiniger Apr 20 at 20:27