The class of the object returned by DirichletRF()
has been renamed from dirichlet_forest to
DirichletRF, and the S3 methods have been renamed
accordingly (predict.dirichlet_forest() is now
predict.DirichletRF(), and likewise for the
print() method). Calls of the form
predict(forest, newdata) are unaffected, but code that
tests the class directly (for example
inherits(x, "dirichlet_forest")) or that calls a method by
its full name must be updated. Forest objects saved from version 0.1.0
will not dispatch to the new methods.
Trees are now grown on resampled data by default. Version 0.1.0
grew every tree on all n observations;
DirichletRF() now defaults to replace = TRUE
with sample.fraction = 0.632. Fitted forests therefore
differ from those produced by 0.1.0 even for the same seed.
The previous behaviour is recovered with
replace = FALSE, sample.fraction = 1, compute.oob = FALSE.
$fitted and $residuals are now computed
from out-of-bag predictions rather than from in-bag training
predictions, so each observation is predicted only by trees it was not
used to grow. These components are NULL when
compute.oob = FALSE, in which case in-bag values can still
be obtained with predict(forest, X).
Out-of-bag estimation is now supported, controlled by the new
replace, sample.fraction, and
compute.oob arguments. $oob contains the OOB
prediction matrix, the OOB Dirichlet parameter estimates, and a scalar
OOB MSE. The full prediction matrix is returned so that alternative
compositional error measures, such as the Aitchison distance, can be
applied directly.
New distributional argument. When TRUE,
leaf sample indices are retained, allowing the forest to estimate the
full conditional distribution of the response as a weighted distribution
over the training data, at the cost of higher memory usage.
New function predict_weights() returns the proximity
weights of new observations over the training sample. These weights can
be used to estimate conditional means, quantiles, and other functionals.
An OOB proximity matrix is also available via $oob$weights
when both distributional = TRUE and
compute.oob = TRUE.
New function sample_conditional() draws
compositional observations from the forest-weighted conditional
distribution at a single covariate point.
Impurity-based feature importance is now returned in
$importance as raw gain, normalised gain, and split counts,
and is summarised by the new importance() method.
New function permutation_importance() computes
permutation-based out-of-bag variable importance, with a scaled variant
accounting for tree-to-tree variability. Aitchison distance (the
default), mean squared error, and KL divergence are supported as loss
functions.
num.cores.