XGBoost Python Feature Walkthrough

This is a collection of examples for using the XGBoost Python package.

Demo for using xgboost with sklearn

Demo for using xgboost with sklearn

Demo for obtaining leaf index

Demo for obtaining leaf index

This script demonstrate how to access the eval metrics

This script demonstrate how to access the eval metrics

Demo for gamma regression

Demo for gamma regression

Demo for boosting from prediction

Demo for boosting from prediction

Demo for using feature weight to change column sampling

Demo for using feature weight to change column sampling

Demo for accessing the xgboost eval metrics by using sklearn interface

Demo for accessing the xgboost eval metrics by using sklearn interface

Demo for GLM

Demo for GLM

Demo for prediction using number of trees

Demo for prediction using number of trees

Getting started with categorical data

Getting started with categorical data

Demo for using cross validation

Demo for using cross validation

Collection of examples for using sklearn interface

Collection of examples for using sklearn interface

Demo for using data iterator with Quantile DMatrix

Demo for using data iterator with Quantile DMatrix

Experimental support for external memory

Experimental support for external memory

Demo for using process_type with prune and refresh

Demo for using `process_type` with `prune` and `refresh`

Getting started with XGBoost

Getting started with XGBoost

Train XGBoost with cat_in_the_dat dataset

Train XGBoost with cat_in_the_dat dataset

A demo for multi-output regression

A demo for multi-output regression

Demo for training continuation

Demo for training continuation

Demo for using and defining callback functions

Demo for using and defining callback functions

Demo for creating customized multi-class objective function

Demo for creating customized multi-class objective function

Demo for defining a custom regression objective and metric

Demo for defining a custom regression objective and metric