v1.1.0

• Added support for categorical splits (models trained with
  enable_categorical). Categorical features are passed as their integer
  category codes in the data slice, the same encoding XGBoost uses internally.

v1.0.0

• Refactored the codebase to allow using the code generation functionality as a
  library.
• Added support for the reg:logistic, binary:logitraw, reg:squarederror,
  reg:linear, reg:absoluteerror, reg:pseudohubererror, and
  reg:quantileerror objectives. The model's base_score is now applied as an
  intercept, and the sigmoid is only applied for the logistic objectives
  (binary:logistic and reg:logistic). Unsupported objectives, multi-output
  models (such as multi-target regression or multi-quantile
  reg:quantileerror), and forest models (num_parallel_tree greater than one)
  trained with early stopping now produce a clear error.

v0.1.0

Initial version