• 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.