This release marks a major milestone for the XGBoost project. In this release, we introduce an experimental support of using JSON for serializing (saving/loading) XGBoost models and related hyperparameters for training. We would like to eventually replace the old binary format with JSON, since it is an open format and parsers are available in many programming languages and platforms. See the documentation for model I/O using JSON. Previously, users often ran into issues where the model file produced by one machine could not load or run on another machine.

This plugin currently works with the CLI version and python version. The maximum number of nodes needed for a given tree depth d is 2d 1 - 1. The maximum number of nodes on any given level is 2d. Data is stored in a sparse format. For example, missing values produced by one hot encoding are not stored.

This page contains a curated list of examples, tutorials, blogs about XGBoost usecases. It is inspired by awesome-MXNet, awesome-php and awesome-machine-learning. Please send a pull request if you find things that belongs to here. This is a list of short codes introducing different functionalities of xgboost packages. Most of examples in this section are based on CLI or python version.