XGBoost Documentation — xgboost 3. 2. 1 documentation XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable It implements machine learning algorithms under the Gradient Boosting framework
Get Started with XGBoost — xgboost 3. 2. 0 documentation This is a quick start tutorial showing snippets for you to quickly try out XGBoost on the demo dataset on a binary classification task Links to Other Helpful Resources
Introduction to Boosted Trees — xgboost 3. 2. 0 documentation Now that you understand what boosted trees are, you may ask, where is the introduction for XGBoost? XGBoost is exactly a tool motivated by the formal principle introduced in this tutorial!
Python Package Introduction — xgboost 3. 2. 0 documentation This document gives a basic walkthrough of the xgboost package for Python The Python package is consisted of 3 different interfaces, including native interface, scikit-learn interface and dask interface
Installation Guide — xgboost 3. 2. 0 documentation The default installation with pip will install the full XGBoost package, including the support for the GPU algorithms and federated learning You may choose to reduce the size of the installed package and save the disk space, by opting to install xgboost-cpu instead:
XGBoost Parameters — xgboost 3. 2. 0 documentation Before running XGBoost, we must set three types of parameters: general parameters, booster parameters and task parameters General parameters relate to which booster we are using to do boosting, commonly tree or linear model
XGBoost Python Package — xgboost 3. 3. 0-dev documentation XGBoost Python Package This page contains links to all the python related documents on python package To install the package, checkout Installation Guide Contents Python Package Introduction Install XGBoost Data Interface Setting Parameters Training Early Stopping Prediction Plotting Scikit-Learn interface Using the Scikit-Learn Estimator
Introduction to Model IO — xgboost 3. 3. 0-dev documentation More details below Before we get started, XGBoost is a gradient boosting library with focus on tree models, which means inside XGBoost, there are 2 distinct parts: The model consisting of trees and Hyperparameters and configurations used for building the model
Notes on Parameter Tuning — xgboost 3. 2. 0 documentation XGBoost can help feature selection by providing both a global feature importance score and sample feature importance with SHAP value Also, there are parameters specifically targeting categorical features, and tasks like survival and ranking