XGBoost Python Feature Walkthrough
This is a collection of examples for using the XGBoost Python package.
Demo for using xgboost with sklearn
This script demonstrate how to access the eval metrics
Demo for boosting from prediction
Demo for accessing the xgboost eval metrics by using sklearn interface
Demo for using feature weight to change column sampling
Demo for prediction using number of trees
Getting started with categorical data
Collection of examples for using sklearn interface
Demo for using cross validation
Experimental support for external memory
Demo for using data iterator with Quantile DMatrix
Demo for using process_type with prune and refresh
Train XGBoost with cat_in_the_dat dataset
Demo for prediction using individual trees and model slices
Collection of examples for using xgboost.spark estimator interface
Demo for training continuation
A demo for multi-output regression
Demo for using and defining callback functions
Demo for creating customized multi-class objective function
Getting started with learning to rank
Demo for defining a custom regression objective and metric