0.4.0

• Fix multi-classification with trees that contain a single leaf
• C++98 compatibility
• Smaller fixes and improvements

0.3.0

• Support Multiclassification with softmax
• Bugfixes

0.2.0

• support integer valued features
• remove restriction to less than 256 input features (see #3)
• added comparison with treelite package (see README.md)

0.0.1

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