XGBoost is a machine learning modeling framework for gradient-boosted decision trees that was first released by Tianqi Chen in 2014. It is a popular choice in the machine learning community for a wide variety of use cases such as data science competitions, benchmarking, and in production machine learning systems.
SigOpt has worked with many customers who leverage SigOpt's experiment management and optimization platform to track and optimize their XGBoost models. Based on customer feedback and our research and development, we integrated XGBoost's learning API into the SigOpt client to offer XGBoost users a seamless path to:
Automatically track hyperparameters, metadata, checkpoints, and metrics in a SigOpt Run with minimal code.
Automatically tune hyperparameters in a SigOpt Experiment with model-aware optimization techniques.