feature importance in mlr3 with iml for classification forests

Question

I calculate feature importance for 2 different types of machine learning models (SVM and Classification Forest). I cannot post the data here, but I describe what I do:

My (classification) task has about 400 observations of 70 variables. Some of them are highly, but nor perfectly correlated

1. I fit the models with

learner_1$train(task)
learner_2$train(task)

where learner1 is a svm and learner 2 is a classification forest.

2. Now, I want to calculate feature importance with iml, so for each of the learners I use the following code (here the code for learner_1)

  
  model_analyzed=Predictor$new(learner_1,
                               data=dplyr::select(task$data(), task$feature_names), 
                               y=dplyr::select(task$data(), task$target_names))
  
  used_features <- task$feature_names
  
  
  effect = FeatureImp$new(model_analyzed, loss="ce", n.repetitions=10, compare="ratio")
  print(effect$plot(features=used_features))

My results are the following

a) For the SVM

b) For the classification forest

I do not understand the second picture:

a) should the "anchor" point not be around 1, as I observe for the SVM? If the ce is not made worse by shuffling for any feature, then the graph shoud show a 1 and not a 0?

b) If all features show a value very close to zero, as I see in the second graph, does it mean that the classification error is zero, if the feature is shuffled? So for each single feature, I would get a perfect model if just this one feature is omitted or shuffled?

I am really confused here, can someone help me understand what happens?