Recommended learners for mlr3
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 a 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).