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Book Overview & Buying
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Table Of Contents
Machine Learning For Dummies
By :
https://github.com/dmlc/xgboost
Even though the book specifically covers GBM in Chapter 18, other types of gradient boosting machines exist that are based on a slightly different set of optimization approaches and cost functions. The XGBoost package enables you to apply GBM to any problem, thanks to its wide choice of objective functions and evaluation metrics. It operates with a variety of languages, including Python, R, Java, and C++.
In spite of the fact that GBM is a sequential algorithm (and thus slower than others that can take advantage of modern multicore computers), XGBoost leverages multithread processing in order to search in parallel for the best splits among the features. The use of multithreading helps XGBoost turn in an unbeatable performance when compared to other GBM implementations, both in R and Python. Because of all that it contains, the full package name is eXtreme Gradient Boosting (or XGBoost for short). You can find complete documentation for this package...
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