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 accessing the xgboost eval metrics by using sklearn interface

Demo for accessing the xgboost eval metrics by using sklearn interface

Demo for using feature weight to change column sampling

Demo for using feature weight to change column sampling

Demo for GLM

Demo for GLM

Demo for prediction using number of trees

Demo for prediction using number of trees

Getting started with XGBoost

Getting started with XGBoost

Collection of examples for using sklearn interface

Collection of examples for using sklearn interface

Getting started with categorical data

Getting started with categorical data

Demo for using cross validation

Demo for using cross validation

Demo for using process_type with prune and refresh

Demo for using process_type with prune and refresh

Demo for prediction using individual trees and model slices

Demo for prediction using individual trees and model slices

Collection of examples for using xgboost.spark estimator interface

Collection of examples for using xgboost.spark estimator interface

Demo for using data iterator with Quantile DMatrix

Demo for using data iterator with Quantile DMatrix

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

Quantile Regression

Quantile Regression

Demo for training continuation

Demo for training continuation

Feature engineering pipeline for categorical data

Feature engineering pipeline for categorical data

Demo for using and defining callback functions

Demo for using and defining callback functions

Experimental support for external memory

Experimental support for external memory

Demo for creating customized multi-class objective function

Demo for creating customized multi-class objective function

Getting started with learning to rank

Getting started with learning to rank

Demo for defining a custom regression objective and metric

Demo for defining a custom regression objective and metric

Experimental support for distributed training with external memory

Experimental support for distributed training with external memory

Demonstration for parsing JSON/UBJSON tree model files

Demonstration for parsing JSON/UBJSON tree model files