# XGBoost Tutorials

This section contains official tutorials inside XGBoost package.
See [Awesome XGBoost](https://github.com/dmlc/xgboost/tree/master/demo) for more resources. Also, don’t miss the feature introductions in each package.

# Contents:

* [Introduction to Boosted Trees](model.html.md)
* [Introduction to Model IO](saving_model.html.md)
* [Slicing Models](slicing_model.html.md)
* [Learning to Rank](learning_to_rank.html.md)
* [DART](dart.html.md)
* [Monotonic Constraints](monotonic.html.md)
* [Feature Interaction Constraints](feature_interaction_constraint.html.md)
* [Survival Analysis with Accelerated Failure Time](aft_survival_analysis.html.md)
* [Categorical Data](categorical.html.md)
* [Multiple Outputs](multioutput.html.md)
* [Random Forests(TM) in XGBoost](rf.html.md)
* [Distributed XGBoost on Kubernetes](kubernetes.html.md)
* [Distributed XGBoost with XGBoost4J-Spark](https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_tutorial.html)
* [Distributed XGBoost with XGBoost4J-Spark-GPU](https://xgboost.readthedocs.io/en/latest/jvm/xgboost4j_spark_gpu_tutorial.html)
* [Distributed XGBoost with Dask](dask.html.md)
* [Distributed XGBoost with PySpark](spark_estimator.html.md)
* [Using XGBoost External Memory Version](external_memory.html.md)
* [C API Tutorial](c_api_tutorial.html.md)
* [Text Input Format of DMatrix](input_format.html.md)
* [Notes on Parameter Tuning](param_tuning.html.md)
* [Custom Objective and Evaluation Metric](custom_metric_obj.html.md)
* [Advanced Usage of Custom Objectives](advanced_custom_obj.html.md)
* [Intercept](intercept.html.md)
* [Privacy Preserving Inference with Concrete ML](privacy_preserving.html.md)