v6.0.0

Changed

• Updated tables dependency to v3.9.x, which fixes issues with installation of the
  package. This requires Python 3.9 or above, however, so we require that here as well.

v5.1.0

Added

• The Boot class now has save and load methods, which uses joblib under the
  hood.

v5.0.0

Changed

• The dataset-specific dependencies are now put in a separate datasets extra, to make
  the core package more lean. You can install the package with all the dependencies
  using pip install doubt[datasets].

v4.5.2

Changed

• Now also allows pandas 2.x.x versions.

v4.5.1

Security

• Updated urllib3 to 2.0.7 due to a security update.

v4.5.0

Added

• Now saves the models during training with a Boot and reuses those during inference,
  speeding up inference. Thanks to @andrepugni for this contribution!

Fixed

• Downgraded tables to 3.7.x to fix an installation bug.
• Downgraded scikit-learn to >=1.1,<1.3, as the decision tree API in v1.3 is
  incompatible with the previous ones. This will be dealt with separately in the
  future.

v4.4.1

Fixed

• When return_all is specified in Boot.predict and multiple samples have been
  inputted, then it now returns an array of shape (num_samples, num_boots) rather
  than the previous (num_boots, num_samples).

v4.4.0

Added

• Added a return_all argument to the Boot.predict method, which will override the
  uncertainty and quantiles arguments and return the raw bootstrap distribution
  over which the quantiles would normally be calculated. This allows other uses of the
  bootstrap distribution than for computing prediction intervals.

v4.3.1

Fixed

• Previously, all the trees in QuantileRegressionForest were the same. This has now
  been fixed. Thanks to @gugerlir for noticing this!
• The random_seed argument in QuantileRegressionTree and QuantileRegressionForest
  has been changed to random_state to be consistent with DecisionTreeRegressor, and
  to avoid an AttributeError when accessing the estimators of a
  QuantileRegressionForest.

v4.3.0

Added

• The QuantileRegressionForest now has a feature_importances_ attribute.