XGBOOST

XGBoost is an open-source software library that focuses on scalability and computational speed. It can be used in multiple programming languages, including Python, R, C++, and Java. The library includes optimization and cross-validation features.

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The XGBoost algorithm can solve both classification and regression problems. It works with unstructured and structured data sets, and can handle different sparsity patterns. It also automatically handles missing values. The algorithm can also work on user-defined prediction problems. The official XGBoost documentation includes a user guide and tutorials. The library has also been used to power several cutting-edge industry applications.

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XGBoost uses a special split finding algorithm that allows it to process enormous amounts of data. It generates different nodes of the tree in parallel, which improves computational performance. In addition, it uses a depth-first approach. It also uses a compressed column format for data. This reduces the disk reading time.

The XGBoost library can be used on a wide range of platforms, including Linux, Windows, and macOS. It also runs on distributed servers. This allows for the training of very large models. The library also provides various interfaces for different programming languages. It is available under the Apache-2 license.

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XGBoost also offers an array of hyperparameters. These parameters are used to control the learning process of the algorithm. The hyperparameters include alpha and reg_lambda. Alpha is the default value for L1 regularization term on weights, and reg_lambda is the default value for L2 regularization term on weights.