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This is the official clone for the implementation of the NIPS18 paper Multi-Layered Gradient Boosting Decision Trees (mGBDT) .

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# Description @kingfengji Thanks for making the code available. I believe that multi-layered decision trees is a very elegant and powerful approach! I was applying your model to the boston...

/opt/conda/lib/python3.7/site-packages/joblib/parallel.py in (.0) 254 with parallel_backend(self._backend, n_jobs=self._n_jobs): 255 return [func(*args, **kwargs) --> 256 for func, args, kwargs in self.items] 257 258 def __len__(self): /kaggle/working/mGBDT/lib/mgbdt/model/online_xgb.py in fit_increment(self, X, y, num_boost_round, params)...

I feel that code written in python 3.5 would likely be compatible with other python 3 versions, are you sure that a build is necessary in 3.5?

How can we adjust it to a regression problem?