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.
Put the changes in their relevant module.
The following estimators and functions, when fit with the same data and parameters, may produce different models from the previous version. This often occurs due to changes in the modelling logic (bug fixes or enhancements), or in random sampling procedures.
models come here
Details are listed in the changelog below.
(While we are trying to better inform users by providing this information, we cannot assure that this list is complete.)
Fix Changed the convention for
ensemble.HistGradientBoostingRegressor. The depth now corresponds to the number of edges to go from the root to the deepest leaf. Stumps (trees with one split) are now allowed. :pr:
16182by Santhosh B
Fix Fixed a bug where if a
sample_weightparameter was passed to the fit method of
linear_model.RANSACRegressor, it would not be passed to the wrapped
base_estimatorduring the fitting of the final model. #15573 by Jeremy Alexandre.
linear_model.RidgeClassifierCVnow does not allocate a potentially large array to store dual coefficients for all hyperparameters during its
fit, nor an array to store all error or LOO predictions unless
True. #15652 by Jérôme Dockès.
model_selection.RandomizedSearchCVyields stack trace information in fit failed warning messages in addition to previously emitted type and details. #15622 by Gregory Morse.