XGBoost is an gradient boosted decision trees implementation designed with speed and performance. Gradient Boosting is a supervised learning algorithm that tries to accurately predict a target variable by combining the estimates from a set of simple and weaker models. It is used on structured and tabular data. It was created from the author's research on variants of tree boosting as a combination of boosted trees with conditional random field. It got popular when the author decided try Higgs Boson Challenge at Kaggle and end up on the 1st of the leaderboard.

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