XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
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XGBoost implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
Official Website Link : https://xgboost.readthedocs.io/en/latest/index.html
Links
Documentation
Documentation for XGBoost
Using XGBoost with Weights & Biases
Using XGBoost with Weights & Biases
Interpretable Credit Scorecards with XGBoost
Learn how to use XGBoost can be used to construct more performant credit scorecards that remain interpretable.
Using XGBoost with W&B in Kaggle
A tutorial on how to use XGBoost for Kaggle competitions using Weights and Biases
Using W&B Sweeps with XGBoost
Learning how to search through high-dimensional hyperparameter spaces of XGBoost to find the most performant model
Tutorial: Regression and Classification on XGBoost
A short tutorial on how you can use XGBoost with code and interactive visualizations.
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