What is XGBoost?

It is a part of the boosting technique in which the selection of the sample is done more intelligently to classify observations. There are interfaces of XGBoost in C++, R, Python, Julia, Java, and Scala. The core functions in XGBoost are implemented in C++, thus it is easy to share models among different interfaces. Based on the statistics from the CRAN mirror, the package has been downloaded more than 81, 000 times. XgBoost modeling consists of two techniques: Bagging and Boosting. 

• Bagging: It is an approach where you can take random data samples, build learning algorithms, and take simple means to find bagging probabilities.
• Boosting: It is an approach where a selection of approaches is made more intelligently i.e. more and more weight is given to classify observations.

XGBoost is a popular machine learning algorithm and it stands for “Extreme Gradient Boosting.” XGBoost is available in various programming languages, including R. An XGBoost is a fast and efficient algorithm. XG Boost works only with numeric variables. and XGBoost is a fast and efficient algorithm and is used by winners of many machine learning competitions. XG Boost works only with numeric variables. It is widely used for both classification and regression tasks.

In this article, we will learn about What is XGBoost? How to use the XGBoost algorithm in R? specifically a dataset from a big mart that stores attributes and various products ad also you will get to know about the features that are important in the XGBoost model.