Wishlist (formerly TODO):
* There are still errors in the classification mode with predictors
having 32 categories. Don't use such predictors for now. I will
try my best to fix it in the next version.
* Implement the new scheme of handling classwt in classification.
* Allow categorical predictors with more than 32 categories.
* Use more compact storage of proximity matrix.
* Allow case weights by using the weights in sampling?
========================================================================
Change in 4.6-6:
* In randomForest(), proximity matrix is not computed correctly if oob.prox=TRUE.
(Thanks to Adele Cutler for the report!)
* In rfcv(), changed the sample-splitting for regression to handle cases
when there are many ties in the data.
* The calculation of importanceSD for classification was missing a divisor.
(Thanks to Abhishek Jaiantilal for the report.)
× The simulated data in unsupervised randomForest() were not in the right range.
(Thanks to Abhishek Jaiantilal for the report.)
* In rfImpute(), padded 1e-8 to the division by sum of proximities to avoid
division by 0.
* Some R code clean up to keep R CMD check happy (complete argument names).
Change in 4.6-6:
* Fixed yet another bug in checking for missing classes in the in-bag sample.
Change in 4.6-5:
* Minor bug fix for drawing samples in classification that was introduced
in 4.6-4. (Thanks to Joran Elias for reporting.)
Changes in 4.6-4:
* Changed the error condition added in 4.6-3 to the case when there are fewer
than two classes present in the in-bag sample.
Changes in 4.6-3:
* Fixed bugs in the tie-breaking code in various places. (Thanks to
Abhishek Jaiantilal and Nathan Longbotham for the report and Abhishek
for the fix.)
* Throw error if some class has no data after 10 sampling attempts in
classification. (Thanks to Abhishek Jaiantilal for the report.)
Changes in 4.6-2:
* Part of the enhancement in 4.5-37 causes segfault in R-2.12.x. That part
has been reverted to the older code for the time being while the problem
is investigated further.
Changes in 4.6-1:
* The package now includes the rfcv() function for feature selection. See
the reference in the help page for details.
* predict.randomForest() was not retaining names of observations in some
cases.
Changes in 4.5-37:
* Many repeated calls to predict.randomForest() should run faster, thanks
to Philip Pham and Rory Martin who pointed out some unnecessary overhead.
Changes in 4.5-36:
* outlier() now works when the input matrix to randomForest() has no
row names. (Reported by Rau Carrio Gaspar.)
* na.roughfix() is now much faster on some data (fixed based on idea from
Hadley Wickham; problem reported by Mike Williamson.)
* Corrected typos in the description of categorical splits in ?getTree.
Changes in 4.5-35:
* Fixed an error in partialPlot.randomForest(). Now the partial
dependence plots for classification data should be more sensible.
(Thanks to Adele Cutler for the bug report and patch.)
* Re-worded part of the help pages regarding variable importance
calculation.
Changes in 4.5-34:
* Fixed infinite loop when randomForest() is called with non-null
maxnodes.
* Fixed a bug in margin.default() that gave nonsensical results.
Changes in 4.5-33:
* Fixed a _long standing_ bug (existed since the original Fortran) in
randomForest(): If importance=TRUE and proximity=TRUE, the proximity
matrix returned is incorrect. Those computed with importance=FALSE, or
with predict.randomForest(..., proximity=TRUE) are correct.
Changes in 4.5-32:
* Fixed a bug in predict.randomForest(..., predict.all=TRUE) introduced in
4.5-31. Added examples in ?predict.randomForest for the options.
Changes in 4.5-31:
* Added a new option `maxnodes' in randomForest() that limits the size of
trees.
* margin() is now generic with a method for randomForest objects.
* Fixed the help page for getTree() about how data are split on numeric
variables (`<=' instead of `<').
* Fixed predict.randomForest() so that if the randomForest object is of
type "regression" and built from the formula interface and newdata
contains NAs, NAs are returned in the corresponding positions (instead
of being dropped altogether).
Change in 4.5-30:
* In regression, cases that had not been out-of-bag now gets NA as prediction
(as in classification).
Change in 4.5-29:
* Fixed a couple of benign errors in help pages spotted by the new Rd parser.
Change in 4.5-28:
* randomForest() would segfault if there are 32-level factors among the
predictor variables.
Changes in 4.5-27:
* Fixed handling of ordered factors in predict.randomForest().
Changes in 4.5-26:
* Fix formula parsing so use of functions in formula won't trigger errors.
* predict.randomForest() did not work when given a matrix without column
names as the newdata.
Changes in 4.5-25:
* In regression, the out-of-bag estimate of MSE and R-squared for the
first few trees (for which not all observations have been OOB yet),
were computed wrong, leading to gross over-estimates of MSEs for the
first few trees. This did not affect the final MSE and R-squared
for the whole forest; i.e., the first few elements of the `mse'
component (and thus the corresponding elements in `rsq') in the
randomForest object are wrong, but others are correct.
(Thanks to Ulrike Gromping for pointing out this problem, as well
as the one fixed in 4.5-24.)
Changes in 4.5-24:
* randomForest.formula() did not exclude terms preceded by -.
Changes in 4.5-23:
* Fixed tuneRF() to work with R version > 2.6.1.
* Make predict.randomForest() more backward compatible with randomForest
objects created from versions older than 4.5-21.
Changes in 4.5-22:
* Allow unsupervised randomForest not to produce a proximity matrix (by
specifying proximity=FALSE), suggested by Nick Crookston.
Changes in 4.5-21:
* The added check for factor level consistency in predictor variables in
4.5-20 was not working for predictors given in a matrix (reported by
Ramon Diaz-Uriate).
Changes in 4.5-20:
* Fixed a memory bug in the C code when the test set is given and
proximity is requested in regression. (Reported by Clayton Springer.)
* Fixed the one-pass random tie-breaking algorithm in various places.
* Added code to check consistency of levels for factors in the predictors,
as well as allowing missing levels of factors and extraneous variables in
predict(..., newdata). (Thanks to Nick Crookston for suggesting a patch.)
Changes in 4.5-19:
* In classification, if sampsize is small and sampling is not stratified,
the actual sample might be larger than specified in some trees. Now fixed.
* Fixed combine() to work on regression randomForest objects and for
cases when ntree is small.
* randomForest.default() for regression was unnecessarily creating
a matrix of 0s for localImportance when importance=TRUE but
localImp=FALSE. (Thanks to Robert McGehee for reporting these
bugs.)
* predict.randomForest(..., nodes=TRUE) now works for regression.
Changes in 4.5-18:
* Added S-PLUS 8 compatibility.
Changes in 4.5-17:
* Added `w' (for weights) to partialPlot.randomForest().
Changes in 4.5-16:
* Fixed some typos in the documentation source files (e.g., \note vs. \notes,
etc).
Changes in 4.5-15:
* Fixed error message call in predict.randomForest().
Changes in 4.5-14:
* varImpPlot() was ignoring the `type' argument.
* "<" was used instead of ".lt." in Fortran code, which is not F77-compliant.
Changes in 4.5-13:
* Fixed a bug in randomForest() when biasCorr=TRUE for regression.
* Fixed bug in predict.randomForest() when newdata is a matrix with no rownames.
Changes in 4.5-12:
* Added the `strata' argument to randomForest, which, in conjunction with
`sampsize', allow sampling (with or without replacement) according to a
strata variable (which can be something other than the class variable).
Currently only works in classification.
Changes in 4.5-11:
* Fixed partialPlot.randomForest() so that if x.var is a character, it's
taken as the name of the variable.
* Clean up code for importance() and varImpPlot() so that if the
randomForest object only contains one importance measure, varImpPlot()
will work as intended.
Changes in 4.5-10:
* Renamed the first argument of randomForest.formula() to `formula', to be
consistent with other formula interfaces.
Changes in 4.5-9:
* Fixed a bug with unsupervised randomForest(..., keep.forest=TRUE).
* Fixed a bug in regression that caused crash when proximiy=TRUE.
* Added `keep.inbag' argument to randomForest(), which, if set to TRUE,
cause randomForest() to return a matrix of indicators that indicate
which case is included in the bootstrap sample to grow the trees.
Changes in 4.5-8:
* Added some code in predict.randomForest() so it works with randomForest
objects created in older versions of the package.
* Fixed randomForest.default() so that getTree() works when the forest
contains only one tree.
* Added the argument `labelVar' (default FALSE) to getTree() for prettier
output.
Changes in 4.5-7:
* Fixed (another!) bug in splitting on categorical variables, especially
impacting data with binary (categorical) variables.
Changes in 4.5-6:
* Fixed a bug introduced in 4.5-2 that used the wrong default class weights.
Changes in 4.5-5:
* Fixed a couple of bugs in C/Fortran code for splitting on categorical
variables in classification trees, which lead to negative or Inf
decrease in the Gini index.
Changes in 4.5-4:
* Fixed a bug in regression when there are categorical predictors. (The
splits can be completely wrong!)
Changes in 4.5-3:
* Fixed predict.randomForest() so that it uses the class labels stored in
the randomForest object (for classification).
Changes in 4.5-2:
* New argument `cutoff' added to predict.randomForest(). The usage is
analogous to the same argument to randomForest().
* Added `palette' and `pch' arguments to MDSplot() to allow more user control.
* In randomForest(), allow the forest to be returned in `unsupervised' mode.
* Fixed some inaccuracies in help pages.
* Fixed the way version number of the package is found at start-up.
Changes in 4.5-1:
* In classification, split on a categorical predictor with more than 10
categories is made more efficient: For two-class problems, the
heuristic presented in Section 4.2.2 of the CART book is used.
Otherwise 512 randomly sampled (not necessarily unique) splits are
tested, instead of all possible splits.
* New function classCenter() has been added. It takes a proximity matrix and
a vector of class labels and compute one prototype per class.
* Added the `Automobile' data from UCI Machine Learning Repository.
* Fixed partialPlot() for categorical predictors (wrong barplot was produced).
* Some re-organization and clean-up of internal C/Fortran code is on-going.
Changes in 4.4-3:
* Added the nPerm argument to randomForest(), which controls the number
of times the out-of-bag part of each variable is permuted, per tree,
for computing variable importance. (Currently only implemented for
regression.)
* When computing the out-of-bag MSE for each tree for assessing variable
importance in regression, the total number of cases was wrongly used as
the divisor.
* Fixed the default and formula methods of randomForest(), so that the
`call' component of the returned object calls the generic.
* The `% increase in MSE' measure of variable importance in regression was
not being computed correctly (should divide sum of squares by number of
out-of-bag samples rather than total number of samples, for each tree).
* Fixed a bug in na.roughfix.default() that gave warning with matrix input.
Changes in 4.4-2:
* Fixed two memory leaks in the regression code (introduced in 4.3-1).
* Fixed a bug that sometimes caused crash in regression when nodesize
is set to something larger than default (5).
* Changed the tree structure in regression slightly: "treemap" is replaced
by "leftDaughter" and "rightDaughter".
Changes in 4.4-1:
* Made slight change in regression code so that it won't split `pure'
nodes. Also fixed the `increase in node purity' importance measure
in regression.
* The outscale option in randomForest() is removed. Use the outlier()
function instead. The default outlier() method can be used with other
proximity/dissimilarity measures.
* More Fortran subroutines migrated to C.
Changes in 4.3-3:
* Fixed randomForest.formula() so that update() will work.
* Fixed up problem in importance(), which was broken in a couple of ways.
Changes in 4.3-2:
* Fixed a bug that caused crashes in classification if test set data
are supplied.
Changes in 4.3-1:
* Fixed bugs in sampling cases and variables without replacement.
* Added the rfNews() function to display the NEWS file. Advertised in
the start up banner.
* (Not user-visible.) Translated regression tree building code from
Fortran to C. One perhaps noticeable change is less memory usage.
Changes in 4.3-0:
* Thanks to Adele Cutler, there's now casewise variable importance
measures in classification. Similar feature is also added for
regression. Use the new localImp option in randomForest().
* The `importance' component of randomForest object has been changed:
The permutation-based measures are not divided by their `standard
errors'. Instead, the `standard errors' are stored in the
`importanceSD' component. One should use the importance() extractor
function rather than something like rf.obj$importance for extracting
the importance measures.
* The importance() extractor function has been updated: If the
permutation-based measures are available, calling importance()
with only a randomForest object returns the matrix of variable
importance measures. There is the `scale' argument, which defaults
to TRUE.
* In predict.randomForest, there is a new argument `nodes' (default to
FALSE). For classification, if nodes=TRUE, the returned object has an
attribute `nodes', which is an n by ntree matrix of terminal node
indicators. This is ignored for regression.
Changes in 4.2-1:
* There is now a package name space. Only generics are exported.
* Some function names have been changed:
partial.plot -> partialPlot
var.imp.plot -> varImpPlot
var.used -> varUsed
* There is a new option `replace' in randomForest() (default to TRUE)
indicating whether the sampling of cases is with or without
replacement.
* In randomForest(), the `sampsize' option now works for both
classification and regression, and indicate the number of cases to be
drawn to grow each tree. For classification, if sampsize is a vector of
length the number of classes, then sampling is stratified by class.
* With the formula interface for randomForest(), the default na.action,
na.fail, is effective. I.e., an error is given if there are NAs present
in the data. If na.omit is desired, it must be given explicitly.
* For classification, the err.rate component of the randomForest object
(and the corresponding one for test set) now is a ntree by (nclass + 1)
matrix, the first column of which contains the overall error rate, and
the remaining columns the class error rates. The running output now
also prints class error rates. The plot method for randomForest will
plot the class error rates as well.
* The predict() method now checks whether the variable names in newdata
match those from the training data (if the randomForest object is not
created from the formula interface).
* partialPlot() and varImpPlot() now have optional arguments xlab, ylab
and main for more flexible labelling. Also, if a factor is given as
the variable, a real bar plot is produced.
* partialPlot() will now remove rows with NAs from the data frame given.
* For regression, if proximity=FALSE, an n by n array of integers is
erroneously allocated but not used (it's only used for proximity
calculation, so not needed otherwise).
* Updated combine() to conform to the new randomForest object.
* na.roughfix() was not working correctly for matrices, which in turns
causes problem in rfImpute().
Changes in 4.1-0:
* In randomForest(), if sampsize is given, the sampling is now done
without replacement, in addition to stratified by class. Therefore
sampsize can not be larger than the class frequencies.
* In classification randomForest, checks are added to avoid trees with
only the root node.
* Fixed a bug in the Fortran code for classification that caused segfault
on some system when encountering a tree with only root node.
* The help page for predict.randomForest() now states the fact that when
newdata is not specified, the OOB predictions from the randomForest
object is returned.
* plot.randomForest() and print.randomForest() were not checking for
existence of performance (err.rate or mse) on test data correctly.