The library provides a system for use in a range of computing environments, not least:

Regularized Gradient Boosting with both L1 and L2 regularization.

Stochastic Gradient Boosting with sub-sampling at the row, column and column per split levels.

Gradient Boosting algorithm also called gradient boosting machine including the learning rate.Three main forms of gradient boosting are supported: The implementation of the model supports the features of the scikit-learn and R implementations, with new additions like regularization. Nevertheless, it does offer a number of advanced features. The library is laser focused on computational speed and model performance, as such there are few frills. Java and JVM languages like Scala and platforms like Hadoop.

R interface as well as a model in the caret package.

Python interface as well as a model in scikit-learn.

C++ (the language in which the library is written).

Specifically, XGBoost supports the following main interfaces: XGBoost is a software library that you can download and install on your machine, then access from a variety of interfaces. Tianqi Chen provides a brief and interesting back story on the creation of XGBoost in the post Story and Lessons Behind the Evolution of XGBoost. It belongs to a broader collection of tools under the umbrella of the Distributed Machine Learning Community or DMLC who are also the creators of the popular mxnet deep learning library. It is an implementation of gradient boosting machines created by Tianqi Chen, now with contributions from many developers. Tianqi Chen, in answer to the question “ What is the difference between the R gbm (gradient boosting machine) and xgboost (extreme gradient boosting)?” on Quora Which is the reason why many people use xgboost. The name xgboost, though, actually refers to the engineering goal to push the limit of computations resources for boosted tree algorithms. XGBoost stands for e Xtreme Gradient Boosting.

Xgboost install linux