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Changelog

Source: NEWS.md

gbm 2.3.0

Bug fixes

  • 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 out-of-bag improvement estimate for distribution = "coxph", which ignored the current model fit; this corrupted gbm.perf(method = "OOB") for Cox models.

  • 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 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.

Other improvements

  • 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.

  • Improved cross-validation handling and vignette corrections (Cox model formulas, normalized discounted cumulative gain formula, and formulas added for the t-distribution, huberized hinge loss, and multinomial deviance).

gbm 2.1.9

CRAN release: 2024-01-10

  • Maintenance update to address new R standards

  • Fixed gbm.more() for multinomial models, corrected multinomial training and validation error reporting, and removed the warning from gbm(distribution = "multinomial").

gbm 2.1.8

CRAN release: 2020-07-15

  • Removed experimental functions shrink.gbm() and shrink.gbm.pred(); the latter seemed broken anyway. Happy to accept a PR if anyone wants to fix them.

gbm 2.1.7

  • Fix Non-file package-anchored link(s) in documentation... warning.

gbm 2.1.6

  • 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.)

  • Multinomial support remains available for backwards compatibility.

  • 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).

Bug fixes

  • 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.

gbm 2.1.5

CRAN release: 2019-01-14

  • 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.

gbm 2.1.4

CRAN release: 2018-09-14

  • 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.