Legend for changelogs¶
Major Feature : something big that you couldn’t do before.
Feature : something that you couldn’t do before.
Efficiency : an existing feature now may not require as much computation or memory.
Enhancement : a miscellaneous minor improvement.
Fix : something that previously didn’t work as documentated – or according to reasonable expectations – should now work.
API Change : you will need to change your code to have the same effect in the future; or a feature will be removed in the future.
Version 1.1.0 of scikit-learn requires python 3.7+, numpy 1.14.6+ and scipy 1.1.0+. Optional minimal dependency is matplotlib 2.2.2+.
Put the changes in their relevant module.
utils.validation._check_sample_weightcan perform a non-negativity check on the sample weights. It can be turned on using the only_non_negative bool parameter. Estimators that check for non-negative weights are updated:
linear_model.LinearRegression(here the previous error message was misleading),
neighbors.KernelDensity. #20880 by Guillaume Lemaitre and András Simon.
Code and Documentation Contributors¶
Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.0, including:
TODO: update at the time of the release.