XGBoost in R?Īnd as you would expect, there is an R package called ‘ xgboost’, which is an interface to the XGBoost library.
Therefore, it helps to reduce overfitting. XGBoost is one of the implementations of Gradient Boosting concept, but what makes XGBoost unique is that it uses “ a more regularized model formalization to control over-fitting, which gives it better performance,” according to the author of the algorithm, Tianqi Chen. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function. Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. Now, what is Gradient Boosting? Here is the best articulation from Wikipedia.
#Gradient boosting portable#
It is built on the principles of gradient boosting framework and designed to “push the extreme of the computation limits of machines to provide a scalable, portable and accurate library.” It can be used for supervised learning tasks such as Regression, Classification, and Ranking. Teams with this algorithm keep winning the competitions. XGBoost (eXtreme Gradient Boosting) is one of the most loved machine learning algorithms at Kaggle. One of my personally favorite features with Exploratory v3.2 we released last week is Extreme Gradient Boosting (XGBoost) model support with ‘ xgboost’ package.
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