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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()
.mlr3::Learner
API.Added references.
add parameter dependencies for xgboost
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