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      • Demo for using xgboost with sklearn
      • Demo for obtaining leaf index
      • This script demonstrate how to access the eval metrics
      • Demo for gamma regression
      • Demo for boosting from prediction
      • Demo for using feature weight to change column sampling
      • Demo for accessing the xgboost eval metrics by using sklearn interface
      • Demo for GLM
      • Demo for prediction using number of trees
      • Getting started with XGBoost
      • Getting started with categorical data
      • Demo for using cross validation
      • Collection of examples for using sklearn interface
      • Demo for using data iterator with Quantile DMatrix
      • Experimental support for external memory
      • Demo for using process_type with prune and refresh
      • Train XGBoost with cat_in_the_dat dataset
      • A demo for multi-output regression
      • Demo for training continuation
      • Demo for using and defining callback functions
      • Demo for creating customized multi-class objective function
      • Demo for defining a custom regression objective and metric
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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 XGBoost

Getting started with XGBoost

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

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

Download all examples in Python source code: examples_python.zip

Download all examples in Jupyter notebooks: examples_jupyter.zip

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