Difference between revisions of "XGBoost; eXtreme Gradient Boosted trees"

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* [[Random Forest (or) Random Decision Forest]]
 
* [[Random Forest (or) Random Decision Forest]]
 
* [[Boosted Random Forest]]
 
* [[Boosted Random Forest]]
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* Three main forms of gradient boosting:
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** Gradient Boosting (GB)
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** Stochastic GB
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** Regularized GB
  
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Random forest (ensemble method) builds multiple decision trees and merges them together to get a more accurate and stable prediction.  Random Forest is a flexible, easy to use machine learning algorithm that produces, even without hyper-parameter tuning, a great result most of the time. It is also one of the most used algorithms, because it’s simplicity and the fact that it can be used for both classification and regression tasks. [http://towardsdatascience.com/the-random-forest-algorithm-d457d499ffcd The Random Forest Algorithm | Niklas Donges @ Towards Data Science]
 
  
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Its name stands for eXtreme Gradient Boosting, it was developed by Tianqi Chen and now is part of a wider collection of open-source libraries developed by the Distributed Machine Learning Community (DMLC). XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. Specifically, it was engineered to exploit every bit of memory and hardware resources for tree boosting algorithms. The implementation of XGBoost offers several advanced features for model tuning, computing environments and algorithm enhancement. It is capable of performing the three main forms of gradient boosting (Gradient Boosting (GB), Stochastic GB and Regularized GB) and it is robust enough to support fine tuning and addition of regularization parameters. According to Tianqi Chen, the latter is what makes it superior and different to other libraries. [http://www.kdnuggets.com/2017/10/xgboost-top-machine-learning-method-kaggle-explained.html XGBoost, a Top Machine Learning Method on Kaggle, Explained  | Ilan Reinstein - KDnuggets]
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Revision as of 15:44, 27 July 2020

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Its name stands for eXtreme Gradient Boosting, it was developed by Tianqi Chen and now is part of a wider collection of open-source libraries developed by the Distributed Machine Learning Community (DMLC). XGBoost is a scalable and accurate implementation of gradient boosting machines and it has proven to push the limits of computing power for boosted trees algorithms as it was built and developed for the sole purpose of model performance and computational speed. Specifically, it was engineered to exploit every bit of memory and hardware resources for tree boosting algorithms. The implementation of XGBoost offers several advanced features for model tuning, computing environments and algorithm enhancement. It is capable of performing the three main forms of gradient boosting (Gradient Boosting (GB), Stochastic GB and Regularized GB) and it is robust enough to support fine tuning and addition of regularization parameters. According to Tianqi Chen, the latter is what makes it superior and different to other libraries. XGBoost, a Top Machine Learning Method on Kaggle, Explained | Ilan Reinstein - KDnuggets

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