Adjust behavior of "positive"
arg for classif.logreg
(#2846)
Consistent naming for dummy feature encoding of variables with different levels count (#2847)
Remove {nodeHarvest} learners (#2841)
Remove {rknn} learner (#2842)
Remove all {DiscriMiner} learners (#2840)
Remove {extraTrees} learner (#2839)
Remove depcrecated {rrlda} learner
Resolve some {ggplot} deprecation warnings
Fixed information.gain
filter calculation.
Before, chi.squared
was calculated even though information.gain
was requested due to a glitch in the filter naming (#2816, @jokokojote)
Make helpLearnerParam()
's HTML parsing more robust (#2843)
Add HTML5 support for help pages
FSelectoRcpp::relief()
. This C++ based implementation of the RelieF filter algorithm is way faster than the Java based one from the {FSelector} package (#2804)FilterWrapper
objectsfix.factors.prediction = TRUE
causes the generation of NAs for new factor levels in prediction (@jakob-r, #2794)newdata
(@jakob-r, #2794)praznik_MRMR
: Remove handling of survival tasks (#2790, @bommert)objective
default from reg:linear
(deprecated) to reg:squarederror
blocking
was set in the Task but blocking.cv
was not set within `makeResampleDesc() (#2788)generateLearningCurveData()
(#2768)getFeatureImportance()
: Account for feature importance weight of linear xgboost modelss
did not match the learner note) (#2747)createSpatialResamplingPlots()
. The package caused issues on R-devel. In addition users should set custom themes by themselves.getNestedTuneResultsOptPathDf()
(#2754)regr_slim
learner due to pkg (flare) being orphaned on CRANclValid::dunn
and its tests (package orphaned) (#2742)tuneThreshold()
now accounts for the direction of the measure.
Beforehand, the performance measure was always minimized (#2732).more.args
for simple filters (@annette987, #2709)print.FeatSelResult()
when bits.to.features is used in selectFeatures()
(#2721)getFeatureImportance()
(#2708)Account for {checkmate} v2.0.0 update (#2734)
Refactor function calls from packages (<pkg::fun>
) within ParamSets (#2730) to avoid errors in listLearners()
if those pkgs are not installed
listLearners()
should not fail if a package is not installed (#2717)clValid::dunn
and its tests (package orphaned) (#2742)<pkg::fun>
) within ParamSets (#2730) to avoid errors in listLearners()
if those pkgs are not installedregr_slim
learner due to pkg (flare) being orphaned on CRANtuneThreshold()
now accounts for the direction of the measure.
Beforehand, the performance measure was always minimized (#2732).print.FeatSelResult()
when bits.to.features is used in selectFeatures()
(#2721)getFeatureImportance()
(#2708)listLearners()
should not fail if a package is not installed (#2717)more.args
for simple filters (@annette987, #2709)n.show
argument had no effect in plotFilterValues()
. Thanks @albersonmiranda. (#2689)PR: #2638 (@pfistl)
Added several learners for regression and classification on functional data
classif.classiFunc.(kernel|knn) (knn/kernel using various semi-metrics)
(classif|regr).FDboost (Boosted functional generalized additive models)
Added preprocessing steps for feature extraction from functional data
extractFDAFourier (Fourier transform)
extractFDAMultiResFeatures (Compute features at multiple resolutions)
Fixed a bug where multiclass to binaryclass reduction techniques did not work with functional data.
Several other minor bug fixes and code improvements
tree_method
(@albersonmiranda, #2701)getFeatureImportance()
now returns a long data.frame with columns variable
and importance
.
Beforehand, a wide data.frame was returned with each variable representing a column (@pat-s, #1755).filterFeatures()
: Arg thresh
was not working correctly when applied to ensemble filters. (@annette987, #2699)classif.xgboost
which prevented passing a watchlist for binary tasks. This was caused by a suboptimal internal label inversion approach. Thanks to @001ben for reporting (#32) (@mllg)fda.usc
learners to work with package version >=2.0glmnet
learners to upstream package version 3.0.0xgboost
learners to upstream version 0.90.2 (@pat-s & @be-marc, #2681)classif.gbm
and regr.gbm
. Specifically, param shrinkage
now defaults to 0.1 instead of 0.001. Also more choices for param distribution
have been added. Internal parallelization by the package is now suppressed (param n.cores
). (@pat-s, #2651)h2o.deeplearning
learners (@albersonmiranda, #2668)configureMlr()
to .onLoad()
, possibly fixing some edge cases (#2585) (@pat-s, #2637)h2o.gbm
learners were not running until wcol
was passed somehow due to an internal bug. In addition, this bug caused another issue during prediction where the prediction data.frame
was somehow formatted as a character rather a numeric. Thanks to @nagdevAmruthnath for bringing this up in #2630.method = "vh"
for filter randomForestSRC_var.select
and return informative error message for not supported values. Also argument conservative
can now be passed. See #2646 and #2639 for more information (@pat-s, #2649)Bugfix: Allow method = "md"
of filter randomForestSRC_var.select
to set the value returned for features below its threshold to NA (Issue #2687)
Bugfix: With the new praznik v7.0.0 release filter praznik_CMIM
does no longer return a result for logical features. See https://gitlab.com/mbq/praznik/issues/19 for more information
data.frame
filter values are now returned in a long (tidy) tibble
. This makes it easier to apply post-processing methods (like group_by()
, etc) (@pat-s, #2456)benchmark()
does not store the tuning results ($extract
slot) anymore by default.
If you want to keep this slot (e.g. for post tuning analysis), set keep.extract = TRUE
.
This change originated from the fact that the size of BenchmarkResult
objects with extensive tuning got very large (~ GB) which can cause memory problems during runtime if multiple benchmark()
calls are executed on HPCs.benchmark()
does not store the created models ($models
slot) anymore by default.
The reason is the same as for the $extract
slot above.
Storing can be enabled using models = TRUE
.generateFeatureImportanceData()
gains argument show.info
which shows the name of the current feature being calculated, its index in the queue and the elapsed time for each feature (@pat-s, #26222)classif.liquidSVM
and regr.liquidSVM
have been removed because liquidSVM
has been removed from CRAN.data.table
s default in rbindlist()
. See #2578 for more information. (@mllg, #2579)regr.randomForest
gains three new methods to estimate the standard error:se.method = "jackknife"
se.method = "bootstrap"
se.method = "sd"
See ?regr.randomForest
for more details.
regr.ranger
relies on the functions provided by the package ("jackknife" and "infjackknife" (default))
(@jakob-r, #1784)regr.gbm
now supports quantile distribution
(@bthieurmel, #2603)classif.plsdaCaret
now supports multiclass classification (@GegznaV, #2621)getClassWeightParam()
now also works for Wrapper* Models and ensemble models (@ja-thomas, #891)getLearnerNote()
to query the "Note" slot of a learner (@alona-sydorova, #2086)e1071::svm()
now only uses the formula interface if factors are present. This change is supposed to prevent from "stack overflow" issues some users encountered when using large datasets. See #1738 for more information. (@mb706, #1740)cluster.MiniBatchKmeans
from package ClusterR (@Prasiddhi, #2554)plotHyperParsEffect()
now supports facet visualization of hyperparam effects for nested cv (@MasonGallo, #1653)data.table
s default in rbindlist()
. See #2578 for more information. (@mllg, #2579)options(on.learner.error)
was not respected in benchmark()
. This caused benchmark()
to stop even if it should have continued including FailureModels
in the result (@dagola, #1984)getClassWeightParam()
now also works for Wrapper* Models and ensemble models (@ja-thomas, #891)getLearnerNote()
to query the "Note" slot of a learner (@alona-sydorova, #2086)praznik_mrmr
also supports regr
and surv
tasksplotFilterValues()
got a bit "smarter" and easier now regarding the ordering of multiple facets. (@pat-s, #2456)filterFeatures()
, generateFilterValuesData()
and makeFilterWrapper()
gained new examples. (@pat-s, #2456)makeResampleDesc(fixed = TRUE)
) (@pat-s, #2412).Task
help pages are now split into separate ones, e.g. RegrTask
, ClassifTask
(@pat-s, #2564)deleteCacheDir()
: Clear the default mlr cache directory (@pat-s, #2463)getCacheDir()
: Return the default mlr cache directory (@pat-s, #2463)getResamplingIndices(inner = TRUE)
now correctly returns the inner indices (before inner indices referred to the subset of the respective outer level train set) (@pat-s, #2413).fw.perc
, fw.abs
or fw.threshold
.
It can be triggered with the new cache
argument in makeFilterWrapper()
or filterFeatures()
(@pat-s, #2463).Additionally, filter names have been harmonized using the following scheme: _. Exeptions are filters included in base R packages. In this case, the package name is omitted.
FSelectorRcpp_gain.ratio
, FSelectorRcpp_information.gain
and FSelectorRcpp_symmetrical.uncertainty
from package FSelectorRcpp
.
These filters are ~ 100 times faster than the implementation of the FSelector
pkg.
Please note that both implementations do things slightly different internally and the FSelectorRcpp
methods should not be seen as direct replacement for the FSelector
pkg.filter names have been harmonized using the following scheme: _. (@pat-s, #2533)
information.gain
-> FSelector_information.gain
gain.ratio
-> FSelector_gain.ratio
symmetrical.uncertainty
-> FSelector_symmetrical.uncertainty
chi.squared
-> FSelector_chi.squared
relief
-> FSelector_relief
oneR
-> FSelector_oneR
randomForestSRC.rfsrc
-> randomForestSRC_importance
randomForestSRC.var.select
-> randomForestSRC_var.select
randomForest.importance
-> randomForest_importance
fixed a bug related to the loading of namespaces for required filter packages (@pat-s, #2483)
"h2o.use.data.table" = TRUE
is now the default (@j-hartshorn, #2508)x.bit.names
that stores the optimal bitsx
now always contains the real feature names and not the bit.namesmakeFeatSelWrapper
usable with custom bit.names
.sffs
crashed in some cases (@bmihaljevic, #2486)resample.fun
to specify a custom resampling function to use.Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.