XGBoost-FastForest

Minimal library code to deploy XGBoost models in C++.

In science, it is very common to protoype algorithms with Python and then put them in production with fast C++ code. Transitioning models from Python to C++ should be as easy as possible to make sure new ideas can be tried out rapidly. The FastForest library helps you to get your xgboost model into a C++ production environment as quickly as possible.

The mission of this library is to be:

• Easy: deploying your xgboost model should be as painless as it can be
• Fast: thanks to efficient data structures for storing the trees, this library goes easy on your CPU and memory
• Safe: the FastForest objects are immutable, and therefore they are an excellent choice in multithreading environments
• Portable: FastForest has no dependency other than the C++ standard library