XGBoost vs Python Sklearn gradient boosted trees I am trying to understand how XGBoost works.  I already understand how gradient boosted trees work on Python sklearn.   What is not clear to me is if XGBoost works the same way, but faster, or if there are fundamental differences between it and the python implementation.
When I read this paper
http://learningsys.org/papers/LearningSys_2015_paper_32.pdf
It looks to me like the end result coming out of XGboost is the same as in the Python implementation, however the main difference is how XGboost finds the best split to make in each regression tree.
Basically, XGBoost gives the same result, but it is faster.
Is this correct, or is there something else I am missing ?
 A: Unlike the Sklearn's gradient boosting, Xgboost does regularization of the tree as well to avoid overfitting and it deals with the missing values efficiently as well. Following link might be helpful to learn xgboost precisely https://www.youtube.com/watch?v=Vly8xGnNiWs 
A: You are correct, XGBoost ('eXtreme Gradient Boosting') and sklearn's GradientBoost are fundamentally the same as they are both gradient boosting implementations.
However, there are very significant differences under the hood in a practical sense. XGBoost is a lot faster (see http://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/) than sklearn's. XGBoost is quite memory-efficient and can be parallelized (I think sklearn's cannot do so by default, I don't know exactly about sklearn's memory-efficiency but I am pretty confident it is below XGBoost's).
Having used both, XGBoost's speed is quite impressive and its performance is superior to sklearn's GradientBoosting.
A: XGboost is implementation of GBDT with randmization(It uses coloumn sampling and row sampling).Row sampling is possible by not using all of the training data for each base model of the GBDT. Instead of using all of the training data for each base-model, we sample a subset of rows and use only those rows of data to build each of the base models. This ensures that there is a lesser chance of overfitting which is a major issue with simple GBDT which XGBoost tries to address using this randomization.
