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Copy file name to clipboardExpand all lines: CONTRIBUTORS.md
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*[Masaaki Horikoshi](https://github.com/sinhrks)
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- Masaaki is the initial creator of xgboost python plotting module.
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*[Hongliang Liu](https://github.com/phunterlau)
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- Hongliang is the maintainer of xgboost python PyPI package for pip installation.
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*[Hyunsu Cho](http://hyunsu-cho.io/)
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- Hyunsu is the maintainer of the XGBoost Python package. He is in charge of submitting the Python package to Python Package Index (PyPI). He is also the initial author of the CPU 'hist' updater.
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*[daiyl0320](https://github.com/daiyl0320)
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- daiyl0320 contributed patch to xgboost distributed version more robust, and scales stably on TB scale datasets.
- Abstract 1D vector class now works with multiple GPUs (#3287)
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- Generate PTX code for most recent architecture (#3316)
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- Fix a memory bug on NVIDIA K80 cards (#3293)
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- Address performance instability for single-GPU, multi-core machines (#3324)
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* Python package
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- FreeBSD support (#3247)
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- Validation of feature names in `Booster.predict()` is now optional (#3323)
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* Updated Sklearn API
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- Validation sets now support instance weights (#2354)
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-`XGBClassifier.predict_proba()` should not support `output_margin` option. (#3343) See BREAKING CHANGES below.
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* R package:
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- Better handling of NULL in `print.xgb.Booster()` (#3338)
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- Comply with CRAN policy by removing compiler warning suppression (#3329)
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- Updated CRAN submission
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* JVM packages
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- JVM packages will now use the same versioning scheme as other packages (#3253)
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- Update Spark to 2.3 (#3254)
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- Add scripts to cross-build and deploy artifacts (#3276, #3307)
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- Fix a compilation error for Scala 2.10 (#3332)
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* BREAKING CHANGES
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-`XGBClassifier.predict_proba()` no longer accepts paramter `output_margin`. The paramater makes no sense for `predict_proba()` because the method is to predict class probabilities, not raw margin scores.
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## v0.71 (2018.04.11)
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* This is a minor release, mainly motivated by issues concerning `pip install`, e.g. #2426, #3189, #3118, and #3194.
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With this release, users of Linux and MacOS will be able to run `pip install` for the most part.
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