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Introduction

XGBoost (short for eXtreme Gradient Boosting) is an efficient general purpose gradient boosting library. Via easy configuration, we can use different boosting models and objective functions to fit the real world data. To get started, read Binary Classification example.

Examples

• Binary Classification (read this for quick start)
• Regression
• Ranking
• See more examples in the demo folder

Information

• Input Data Format
• Python Interface

Questions

If you have questions, suggestions, check out existing questions and fire an issue:)

Boosters

• Tree Booster
• Linear Booster

Parameter Setting

• Detailed parameter settings are provided in Parameters