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LearnerClassifXgboost
and
LearnerRegrXgboost
now support internal tuning and
validation. This now also works in conjunction with
mlr3pipelines
.nnet
learner and support
feature type "integer"
.min.bucket
parameter to
classif.ranger
and regr.ranger
.mlr3learners
removes learners from
dictionary.regr.nnet
learner.classif.log_reg
.default_values()
function for ranger and svm
learners.eval_metric()
is now explicitly set for xgboost
learners to silence a deprecation warning.mtry.ratio
is
converted to mtry
to simplify tuning.glm
and glmnet
(#199). While
predictions in previous versions were correct, the estimated
coefficients had the wrong sign.lambda
and s
for
glmnet
learners (#197).glmnet
now support to extract
selected features (#200).kknn
now raise an exception if
k >= n
(#191).ranger
now come with the virtual
hyperparameter mtry.ratio
to set the hyperparameter
mtry
based on the proportion of features to use.$loglik()
), allowing to calculate measures like
AIC or BIC in mlr3
(#182).e1071
.set_threads()
in mlr3 provides a generic way to set the
respective hyperparameter to the desired number of parallel
threads.survival:aft
objective to
surv.xgboost
predict.all
from ranger learners
(#172).surv.ranger
, c.f.
https://github.com/mlr-org/mlr3proba/issues/165.classif.nnet
learner (moved from
mlr3extralearners
).LearnerSurvRanger
.glmnet
tests on solaris.bibtex
.classif.glmnet
and
classif.cv_glmnet
with predict_type
set to
"prob"
(#155).glmnet
to be more robust if
the order of features has changed between train and predict.$model
slot of the {kknn} learner now returns a
list containing some information which is being used during the predict
step. Before, the slot was empty because there is no training step for
kknn.saveRDS()
, serialize()
etc.penalty.factor
is a vector param, not
a ParamDbl
(#141)mxitnr
and epsnr
from
glmnet v4.0 updatesurv.glmnet
(#130)mlr3proba
(#144)surv.xgboost
(#135)surv.ranger
(#134)cv_glmnet
and
glmnet
(#99)predict.gamma
and
newoffset
arg (#98)inst/paramtest
was
added. This test checks against the arguments of the upstream train
& predict functions and ensures that all parameters are implemented
in the respective mlr3 learner (#96).interaction_constraints
to {xgboost}
learners (#97).classif.multinom
from package
nnet
.regr.lm
and classif.log_reg
now
ignore the global option "contrasts"
.additional-learners.Rmd
listing all mlr3
custom learnersinteraction_constraints
(#95)logical()
to multiple
learners.regr.glmnet
, regr.km
,
regr.ranger
, regr.svm
,
regr.xgboost
, classif.glmnet
,
classif.lda
, classif.naivebayes
,
classif.qda
, classif.ranger
and
classif.svm
.glmnet
: Added relax
parameter (v3.0)xgboost
: Updated parameters for v0.90.0.2*.xgboost
and *.svm
which
was triggered if columns were reordered between $train()
and $predict()
.Changes to work with new mlr3::Learner
API.
Improved documentation.
Added references.
add new parameters of xgboost version 0.90.2
add parameter dependencies for xgboost
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.