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Release version 0.71 (#3200)

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29
NEWS.md
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@ -3,7 +3,34 @@ XGBoost Change Log
This file records the changes in xgboost library in reverse chronological order. This file records the changes in xgboost library in reverse chronological order.
* BREAKING CHANGES: Updated linear modelling algorithms. In particular L1/L2 regularisation penalties are now normalised to number of training examples. This makes the implementation consistent with sklearn/glmnet. L2 regularisation has also been removed from the intercept. To produce linear models with the old regularisation behaviour, the alpha/lambda regularisation parameters can be manually scaled by dividing them by the number of training examples. ## v0.71 (2018.04.11)
* This is a minor release, mainly motivated by issues concerning `pip install`, e.g. #2426, #3189, #3118, and #3194.
With this release, users of Linux and MacOS will be able to run `pip install` for the most part.
* Refactored linear booster class (`gblinear`), so as to support multiple coordinate descent updaters (#3103, #3134). See BREAKING CHANGES below.
* Fix slow training for multiclass classification with high number of classes (#3109)
* Fix a corner case in approximate quantile sketch (#3167). Applicable for 'hist' and 'gpu_hist' algorithms
* Fix memory leak in DMatrix (#3182)
* New functionality
- Better linear booster class (#3103, #3134)
- Pairwise SHAP interaction effects (#3043)
- Cox loss (#3043)
- AUC-PR metric for ranking task (#3172)
- Monotonic constraints for 'hist' algorithm (#3085)
* GPU support
- Create an abtract 1D vector class that moves data seamlessly between the main and GPU memory (#2935, #3116, #3068). This eliminates unnecessary PCIe data transfer during training time.
- Fix minor bugs (#3051, #3217)
- Fix compatibility error for CUDA 9.1 (#3218)
* Python package:
- Correctly handle parameter `verbose_eval=0` (#3115)
* R package:
- Eliminate segmentation fault on 32-bit Windows platform (#2994)
* JVM packages
- Fix a memory bug involving double-freeing Booster objects (#3005, #3011)
- Handle empty partition in predict (#3014)
- Update docs and unify terminology (#3024)
- Delete cache files after job finishes (#3022)
- Compatibility fixes for latest Spark versions (#3062, #3093)
* BREAKING CHANGES: Updated linear modelling algorithms. In particular L1/L2 regularisation penalties are now normalised to number of training examples. This makes the implementation consistent with sklearn/glmnet. L2 regularisation has also been removed from the intercept. To produce linear models with the old regularisation behaviour, the alpha/lambda regularisation parameters can be manually scaled by dividing them by the number of training examples.
## v0.7 (2017.12.30) ## v0.7 (2017.12.30)
* **This version represents a major change from the last release (v0.6), which was released one year and half ago.** * **This version represents a major change from the last release (v0.6), which was released one year and half ago.**

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R-package/DESCRIPTION
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Package: xgboost Package: xgboost
Type: Package Type: Package
Title: Extreme Gradient Boosting Title: Extreme Gradient Boosting
Version: 0.7.0.1 Version: 0.71.1
Date: 2018-02-25 Date: 2018-04-11
Authors@R: c( Authors@R: c(
person("Tianqi", "Chen", role = c("aut"), person("Tianqi", "Chen", role = c("aut"),
email = "tianqi.tchen@gmail.com"), email = "tianqi.tchen@gmail.com"),

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python-package/xgboost/VERSION
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0.7 0.71