CNode‘s cN field (number of
training observations in a node). A tree’s root node never had
cN set explicitly; this was harmless whenever the tree
split at least once (the root is discarded as soon as it splits), but on
the rare tree that fails to find any improving split, the root remains
the sole terminal node and several distributions’
FitBestConstant()/Adjust() read its
cN before anything had written to it. Fixed in three
places: the root node’s cN is now set explicitly when
created (src/tree.cpp); CNode’s constructor
now initializes cN (fixes first use of any node); and the
node factory’s recycling pool now resets cN on every reuse
(src/node_factory.cpp), preventing a stale count from a
node’s previous use from silently persisting into its next one.Fixed predictions from models with categorical (factor) splits on
platforms where plain char is unsigned (notably Linux on
ARM64/aarch64). The categorical split-direction codes were stored in
char and the value -1 became 255,
silently routing all left-branch observations to the missing branch in
predict() and plot(). Model training was
unaffected; only predictions from the stored model object were wrong,
and only on affected platforms.
Fixed the multinomial deviance calculation, which was one iteration behind because it used cached class probabilities; also fixed the corresponding out-of-bag improvement calculation, which was double-counting updates.
Fixed gbm.more() for
distribution = "multinomial": continuing training now
correctly reconstructs the class ordering and data for both
keep.data = TRUE and keep.data = FALSE, passes
along the prior fit safely, restores the fit as an n x K matrix, and
treats n.trees as boosting iterations rather than a raw
class-tree count.
Fixed two bugs in gbm.more() for
distribution = "coxph": observation weights could be
silently replaced with the predictor matrix in certain cases, and
cRows/cCols were not available yet when first
needed.
Fixed the out-of-bag improvement estimate for
distribution = "coxph", which ignored the current model
fit; this corrupted gbm.perf(method = "OOB") for Cox
models.
Fixed observation weights not being reordered along with the data
in gbmCrossVal()’s per-fold fitting, which corrupted
weighted cross-validation.
InitF() is now always called when fitting, including
when extending an existing model via gbm.more(), so
distributions can allocate any iteration-specific internal buffers they
need.
Fixed offset handling in several places: offsets are now
correctly reordered alongside the data for
distribution = "pairwise";
distribution = "huberized" now includes the offset in its
terminal-node estimates; distribution = "poisson" now
applies its prediction clamp when an offset is supplied; and supplying a
real offset vector no longer errors in gbm.fit() and
gbm.more() for Cox models.
permutation.test.gbm() now works for all
distributions, not just "pairwise".
Fixed undefined behavior in
distribution = "pairwise" model fitting where internal
buffers were reserved but not actually resized before being written
to.
Fixed a potential stack overflow in plot.gbm() for
very deep trees.
Fixed distribution = "quantile" to handle
observation weights.
Fixed Poisson terminal-node predictions when a node’s denominator is zero.
gbm() now raises an informative error when a
supplied weights vector’s length doesn’t match the data,
instead of failing obscurely later on.
plot.gbm() no longer errors if the
viridis package isn’t installed; it now falls back to a
built-in color palette.
Added the missing y-axis label for
distribution = "huberized" plots.
gbm(distribution = "multinomial") no longer emits the
“ill-advised… currently broken” warning added in 2.1.6. Multinomial
support has been fixed and is now tested. Note this removes a warning
that some downstream packages’ tests may assert on (see
pmml, below).distribution = "adaboost" now uses the negative
gradient as its working response, consistent with the other
distributions; among other things this makes var.monotone
constraints act in the intended direction for AdaBoost models.
Switched the test suite from tinytest to testthat.
Vignette corrections: Cox model formulas, the normalized discounted cumulative gain formula, and formulas added for the t-distribution, huberized hinge loss, and multinomial deviance; converted the vignette build from Sweave to R Markdown and added a pkgdown site.
shrink.gbm() and
shrink.gbm.pred(); the latter seemed broken anyway. Happy
to accept a PR if anyone wants to fix them.Non-file package-anchored link(s) in documentation...
warning.Corrected the number of arguments for
gbm_shrink_gradient() in gbm-init.c (#50).
(Thanks to CRAN for highlighting the issue.)
Removed unnecessary dependency on gridExtra.
Switched to using lapply() instead of
parallel::parLapply() whenever
n.cores = 1.
Calling gbm() with
distribution = "bernoulli" will now throw an error whenever
the response is non-numeric (e.g., 0/1 factors will throw an error
instead of possibly crashing the session.) (#6). (Thanks
to @mzoll.)
Calling gbm() with
distribution = "multinomial" now comes with a warning
message; multinomial support has always been problematic and since this
package is only being maintained for backwards compatibility, it likely
will not be fixed unless someone makes a PR.
Switched from RUnit to tinytest
framework. The test.gbm(),
test.relative.influence(), and validate.gbm()
functions will remain for backwards compatability. This is just the
start, as more tests will be added in the future (#51).
Fixed a long standing bug that could occur when using k-fold cross-validation with a response that’s been transformed in the model formula (#30).
Fixed a but that would crash the session when giving “bad” input
for n.trees in the call to predict.gbm() (#45).
(Thanks to @ngreifer.)
Fixed a bug where calling predict() could throw an
error in some cases when n.trees was not
specified.
Fixed bug that occurred whenever distribution was a
list (e.g., “pairwise” regression) (#27).
Fixed a bug that occurred when making predictions on new data with different factor levels (#28).
Fixed a bug that caused relative.influence() to give
different values whenever n.trees was/wasn’t given for
multinomial distributions (#31).
The plot.it argument of gbm.perf() is
no longer ignored (#34).
Fixed an error that occurred in gbm.perf() whenever
oobag.curve = FALSE and
overlay = FALSE.
Switched from CHANGES to NEWS
file.
Updated links and maintainer field in DESCRIPTION
file.
Fixed bug caused by factors with unused levels (#5).
Fixed bug with axis labels in the plot() method for
"gbm" objects (#17).
The plot() method for "gbm" objects is
now more consistent and always returns a "trellis" object
(#19).
Consequently, setting graphical parameters via par will no
longer have an effect on the output from
plot.gbm().
The plot() method for "gbm" objects
gained five new arguments: level.plot,
contour, number, overlap, and
col.regions; see ?plot.gbm for
details.
The default color palette for false color level plots in
plot.gbm() has changed to the Matplotlib ‘viridis’ color
map.
Fixed a number of references and URLs.
Need a high-speed mirror for your open-source project?
Contact our mirror admin team at info@clientvps.com.
This archive is provided as a free public service to the community.
Proudly supported by infrastructure from VPSPulse , RxServers , BuyNumber , UnitVPS , OffshoreName and secure payment technology by ArionPay.