Version 2.15
mlr 2.15.0
CRAN release: 2019-08-06
Breaking
- Instead of a wide
data.frame
filter values are now returned in a long (tidy)tibble
. This makes it easier to apply post-processing methods (likegroup_by()
, etc) (@pat-s, #2456) -
benchmark()
does not store the tuning results ($extract
slot) anymore by default. If you want to keep this slot (e.g. for post tuning analysis), setkeep.extract = TRUE
. This change originated from the fact that the size ofBenchmarkResult
objects with extensive tuning got very large (~ GB) which can cause memory problems during runtime if multiplebenchmark()
calls are executed on HPCs. -
benchmark()
does not store the created models ($models
slot) anymore by default. The reason is the same as for the$extract
slot above. Storing can be enabled usingmodels = TRUE
.
functions - general
-
generateFeatureImportanceData()
gains argumentshow.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)
learners - general
-
classif.liquidSVM
andregr.liquidSVM
have been removed becauseliquidSVM
has been removed from CRAN. - fixed a bug that caused an incorrect aggregation of probabilities in some cases. The bug existed since quite some time and was exposed due to the change of
data.table
s default inrbindlist()
. See #2578 for more information. (@mllg, #2579) -
regr.randomForest
gains three new methods to estimate the standard error: -
regr.gbm
now supportsquantile distribution
(@bthieurmel, #2603) -
classif.plsdaCaret
now supports multiclass classification (@GegznaV, #2621)
functions - general
-
getClassWeightParam()
now also works for Wrapper* Models and ensemble models (@ja-thomas, #891) - added
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)
learners - new
- add learner
cluster.MiniBatchKmeans
from package ClusterR (@Prasiddhi, #2554)
function - general
-
plotHyperParsEffect()
now supports facet visualization of hyperparam effects for nested cv (@MasonGallo, #1653) - fixed a bug that caused an incorrect aggregation of probabilities in some cases. The bug existed since quite some time and was exposed due to the change of
data.table
s default inrbindlist()
. See #2578 for more information. (@mllg, #2579) - fixed a bug in which
options(on.learner.error)
was not respected inbenchmark()
. This causedbenchmark()
to stop even if it should have continued includingFailureModels
in the result (@dagola, #1984) -
getClassWeightParam()
now also works for Wrapper* Models and ensemble models (@ja-thomas, #891) - added
getLearnerNote()
to query the “Note” slot of a learner (@alona-sydorova, #2086)
filters - general
- Filter
praznik_mrmr
also supportsregr
andsurv
tasks -
plotFilterValues()
got a bit “smarter” and easier now regarding the ordering of multiple facets. (@pat-s, #2456) -
filterFeatures()
,generateFilterValuesData()
andmakeFilterWrapper()
gained new examples. (@pat-s, #2456)
filters - new
- Ensemble features are now supported. These filters combine multiple single filters to create a final ranking based on certain statistical operations. All new filters are listed in a dedicated section “ensemble filters” in the tutorial. Tuning of simple features is not supported yet because of a missing feature in ParamHelpers. (@pat-s, #2456)