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PDF XGBoost: A Scalable Tree Boosting System

In this paper we describe a scalable end- to-end tree boosting system called XGBoost which is used widely by data scientists to achieve state-of-the-art 

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  • XGBoost stands for Extreme Gradient Boosting, which applies a Gradient Boosting technique based on decision trees.
    It constructs short, basic decision trees iteratively.
    Each tree is termed as a “weak learner” because of its high bias.
    XGBoost begins by building the first basic tree that has a poor performance.

  • What is the use of XGBoost?

    XGBoost is a boosting algorithm.
    It takes in training data, uses it to train a model, and then evaluates the model on new data.
    This process repeats until the model stops improving.

  • Why does XGBoost work so well?

    3.
    Regularization: XGBoost incorporates both L1 (Lasso regression) and L2 (Ridge regression) regularization techniques, which help prevent overfitting by penalizing complex models.
    This feature enables the algorithm to produce more generalizable models, making it suitable for a wide range of business applications.

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