Last version of 0.4x

This is last version release of 0.4 series, with many changes in the language bindings.

This is also a checkpoint before we switch to xgboost-brick #736

Changes

• Changes in R library
  • fixed possible problem of poisson regression.
  • switched from 0 to NA for missing values.
  • exposed access to additional model parameters.
• Changes in Python library
  • throws exception instead of crash terminal when a parameter error happens.
  • has importance plot and tree plot functions.
  • accepts different learning rates for each boosting round.
  • allows model training continuation from previously saved model.
  • allows early stopping in CV.
  • allows feval to return a list of tuples.
  • allows eval_metric to handle additional format.
  • improved compatibility in sklearn module.
  • additional parameters added for sklearn wrapper.
  • added pip installation functionality.
  • supports more Pandas DataFrame dtypes.
  • added best_ntree_limit attribute, in addition to best_score and best_iteration.
• Java api is ready for use
• Added more test cases and continuous integration to make each build more robust.

XGBoost-0.4-0

This is a stable release of 0.4 version

XGBoost-0.3-2

This is stable version release, corresponding to the R-package xgboost-0.3-2. With new demo folder for detailed walk through.

Latest version of 0.2x

This is latest version of 0.2x, used to document before changing the code into xgboost-unity

Second release, with BugFix

Fix a major bug in v0.2 #8 and #10 .
scale_pos_weight is not properly initialized to default.
The bug will cause binary classification model work improperly when scale_pos_weight is not set in the parameter.

But if the scale_pos_weight is set properly, the result will be correct in v0.2

first release

XGBoost with regression and binary classification support