Bringing back uncertainty to machine learning.
Boot
and reuses those during inference,
speeding up inference. Thanks to @andrepugni for this contribution!tables
to 3.7.x to fix an installation bug.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.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.QuantileRegressionForest
were the same. This has now
been fixed. Thanks to @gugerlir for noticing this!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
.