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quantile-forest

Release Notes#

Version 1.3.5 (released Apr 15, 2024)#

  • Fix fractional max_samples (#47)

  • Add support for monotonic_cst (#49)

Version 1.3.4 (released Feb 21, 2024)#

  • Reorder multi-target outputs (#35)

  • Add tests for model serialization (#36)

  • Update and fix documentation and examples

Version 1.3.3 (released Feb 16, 2024)#

  • Set default value of weighted_leaves at prediction time to False (#34)

  • Update and fix documentation and examples

Version 1.3.2 (released Feb 15, 2024)#

  • Fix bug in multi-target output when max_samples_leaf > 1 (#30)

  • Update quantile forest examples (#31)

  • Update and fix documentation (#33)

Version 1.3.1 (released Feb 12, 2024)#

  • Fix single-output performance regression (#29)

Version 1.3.0 (released Feb 11, 2024)#

  • Support for multiple-output quantile regression (#26)

  • Update conformalized quantile regression example (#28)

Version 1.2.5 (released Feb 10, 2024)#

  • Fix weighted leaf and quantile bug (#27)

Version 1.2.4 (released Jan 16, 2024)#

  • Use base model parameter validation when available

  • Resolve Cython 3 deprecation warnings

Version 1.2.3 (released Oct 09, 2023)#

  • Fix bug that could prevent interpolation from being correctly applied (#15)

  • Update documentation and docstrings

Version 1.2.2 (released Oct 08, 2023)#

  • Optimize performance for predictions when max_samples_leaf = 1 (#13)

  • Update documentation and examples (#14)

Version 1.2.1 (released Oct 04, 2023)#

  • More efficient calculation of weighted quantiles (#11)

  • Add support for Python version 3.12

Version 1.2.0 (released Aug 01, 2023)#

  • Add optional default_quantiles parameter to the model initialization

  • Update documentation

Version 1.1.3 (released Jul 08, 2023)#

  • Fix building from the source distribution

  • Minor update to documentation

Version 1.1.2 (released Mar 22, 2023)#

  • Fix for compatibility with development version of scikit-learn

  • Update documentation and examples

Version 1.1.1 (released Dec 19, 2022)#

  • Fix for compatibility with scikit-learn 1.2.0

  • Fix to documentation

  • Update version requirements

Version 1.1.0 (released Nov 07, 2022)#

  • Update default max_samples_leaf to 1 (previously None)

  • Update documentation and unit tests

  • Miscellaneous update for compatibility with scikit-learn >= 1.1.0

This version supports Python versions 3.8 to 3.11. Note that support for 32-bit Python on Windows has been dropped in this release.

Version 1.0.2 (released Mar 28, 2022)#

  • Add sample weighting by leaf size

Version 1.0.1 (released Mar 23, 2022)#

  • Suppresses UserWarning

Version 1.0.0 (released Mar 23, 2022)#

Initial release.