In recent times, ensemble techniques have become popular among data scientists and enthusiasts. Until now Random Forest and Gradient Boosting algorithms were winning the data science competitions and hackathons, over the period of the last few years XGBoost has been performing better than other algorithms on problems involving structured data. Apart from its performance, XGBoost is also recognized for its speed, accuracy and scale. XGBoost is developed on the framework of Gradient Boosting.

Just like other boosting algorithms XGBoost uses decision trees for its ensemble model. Each tree is a weak learner. The algorithm goes on by sequentially building more decision trees, each one correcting the error of the previous tree until a stopping condition is reached.

In this article, we will discuss the implementation of XGBoost Algorithm in R.

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