* Add a new API function for predicting on `DMatrix`. This function aligns with rest of the `XGBoosterPredictFrom*` functions on semantic of function arguments. * Purge `ntree_limit` from libxgboost, use iteration instead. * [dask] Use `inplace_predict` by default for dask sklearn models. * [dask] Run prediction shape inference on worker instead of client. The breaking change is in the Python sklearn `apply` function, I made it to be consistent with other prediction functions where `best_iteration` is used by default.
======================
XGBoost Python Package
======================
|PyPI version|
Installation
============
From `PyPI <https://pypi.python.org/pypi/xgboost>`_
---------------------------------------------------
For a stable version, install using ``pip``::
pip install xgboost
.. |PyPI version| image:: https://badge.fury.io/py/xgboost.svg
:target: http://badge.fury.io/py/xgboost
For building from source, see `build <https://xgboost.readthedocs.io/en/latest/build.html>`_.