Повторное резюме в ансамблевой модели mrl3

У меня есть красивая модель ансамбля mlr3 (объединенная glmnet и glm) для двоичного прогнозирования, см. Подробности здесь

library("mlr3verse")
library("dplyr")

# get example data
data(PimaIndiansDiabetes, package="mlbench")
data <- PimaIndiansDiabetes

# add an additional predictor "superdoc" which is not entered in the glmnet but in the final glm
set.seed(2323)
data %>% 
  rowwise() %>% 
  mutate(superdoc=case_when(diabetes=="pos" ~ as.numeric(sample(0:2,1)), TRUE~ 0)) %>% 
  ungroup -> data

# make a rather small train set
set.seed(23)
test.data <- sample_n(data,70,replace=FALSE)

# creat elastic net regression
glmnet_lrn = lrn("classif.cv_glmnet", predict_type = "prob")

# create the learner out-of-bag predictions
glmnet_cv1 = po("learner_cv", glmnet_lrn, id = "glmnet")

# PipeOp that drops 'superdoc', i.e. selects all except 'superdoc'
# (ID given to avoid ID clash with other selector)
drop_superdoc = po("select", id = "drop.superdoc",
                   selector = selector_invert(selector_name("superdoc")))

# PipeOp that selects 'superdoc' (and drops all other columns)
select_superdoc = po("select", id = "select.superdoc",
                     selector = selector_name("superdoc"))

# superdoc along one path, the fitted model along the other
stacking_layer = gunion(list(
  select_superdoc,
  drop_superdoc %>>% glmnet_cv1
)) %>>% po("featureunion", id = "union1")

# final logistic regression
log_reg_lrn = lrn("classif.log_reg", predict_type = "prob")

# combine ensemble model
ensemble = stacking_layer %>>% log_reg_lrn


#define tests
train.task <- TaskClassif$new("test.data", test.data, target = "diabetes")

# make ensemble learner
elearner = as_learner(ensemble)

   

ensemble$plot(html = FALSE)

If I train it with different set.seed, I get different coefficients. I think this is mainly caused by the rather low number of training data that is entered in the glmnet model and could be migitated by repeated cross-validation.

# Train the Learner:
# seed 1
elearner = as_learner(ensemble)
set.seed(22521136)
elearner$train(train.task) -> seed1

# seed 2
elearner = as_learner(ensemble)
set.seed(12354)
elearner$train(train.task) -> seed2

# different coefficients of the glment  model
coef(seed1$model$glmnet$model, s ="lambda.min")
#> 9 x 1 sparse Matrix of class "dgCMatrix"
#>                        1
#> (Intercept) -6.238598277
#> age          .          
#> glucose      0.023462376
#> insulin     -0.001007037
#> mass         0.055587740
#> pedigree     0.322911217
#> pregnant     0.137419564
#> pressure     .          
#> triceps      .
coef(seed2$model$glmnet$model, s ="lambda.min")
#> 9 x 1 sparse Matrix of class "dgCMatrix"
#>                        1
#> (Intercept) -6.876802620
#> age          .          
#> glucose      0.025601712
#> insulin     -0.001500856
#> mass         0.063029550
#> pedigree     0.464369417
#> pregnant     0.155971123
#> pressure     .          
#> triceps      .

# different coefficients of the final regression model
seed1$model$classif.log_reg$model$coefficients
#>     (Intercept)        superdoc glmnet.prob.neg glmnet.prob.pos 
#>       -9.438452       23.710923        8.726956              NA
seed2$model$classif.log_reg$model$coefficients
#>     (Intercept)        superdoc glmnet.prob.neg glmnet.prob.pos 
#>       0.3698143      23.5362542      -5.5514365              NA

Вопрос:

Где и как можно ввести повторную перекрестную проверку в моей mlr3 ансамблевой модели для переноса этих различных результатов? Любая помощь очень ценится.