diff --git a/src/Trees/AbstractTrees_BetaML_interface.jl b/src/Trees/AbstractTrees_BetaML_interface.jl
index 313cf952..85ffb84a 100644
--- a/src/Trees/AbstractTrees_BetaML_interface.jl
+++ b/src/Trees/AbstractTrees_BetaML_interface.jl
@@ -42,7 +42,7 @@ In case of a `BetaML/DecisionTree` this is typically a list of feature names as
 wrap(node::DecisionNode, info::NamedTuple = NamedTuple()) = InfoNode(node, info)
 wrap(leaf::Leaf,         info::NamedTuple = NamedTuple()) = InfoLeaf(leaf, info)
 wrap(mod::DecisionTreeEstimator, info::NamedTuple = NamedTuple()) = wrap(mod.par.tree, info)
-wrap(m::Union{DecisionNode,Leaf,DecisionTreeEstimator};feature_names=[]) = wrap(m,(featurenames=feature_names,))
+wrap(m::Union{DecisionNode,Leaf,DecisionTreeEstimator};features_names=[]) = wrap(m,(features_names=features_names,))
 
 
 
@@ -58,7 +58,7 @@ AbstractTrees.children(node::InfoLeaf) = ()
 function AbstractTrees.printnode(io::IO, node::InfoNode)
     q = node.node.question
     condition = isa(q.value, Number) ?  ">=" : "=="
-    col = :featurenames ∈ keys(node.info) ? node.info.featurenames[q.column] : q.column
+    col = :features_names ∈ keys(node.info) ? node.info.features_names[q.column] : q.column
     print(io, "$(col) $condition $(q.value)?")
 end
 
diff --git a/src/Trees/DecisionTrees.jl b/src/Trees/DecisionTrees.jl
index b08c5f5d..eb34b7ec 100644
--- a/src/Trees/DecisionTrees.jl
+++ b/src/Trees/DecisionTrees.jl
@@ -253,8 +253,8 @@ Dict{String, Any}("job_is_regression" => 1, "fitted_records" => 6, "max_reached_
 --> False:  3.3999999999999995
 
 using Plots, TreeRecipe
-feature_names = ["Something", "Som else"]
-wrapped_tree  = wrap(dtree, feature_names = feature_names) # feature_names is otional
+features_names = ["Something", "Som else"]
+wrapped_tree   = wrap(dtree, features_names = features_names) # feature_names is otional
 plot(wrapped_tree)    
 ````
 ![DT plot](assets/dtplot.png) 
diff --git a/test/Trees_tests_additional.jl b/test/Trees_tests_additional.jl
index d7e9c7b1..80a95c13 100644
--- a/test/Trees_tests_additional.jl
+++ b/test/Trees_tests_additional.jl
@@ -56,8 +56,8 @@ tree = Tree()
     yhat_train = Trees.fit!(model, xtrain, ytrain)
 
     println("--> add information about feature names")
-    feature_names = ["Color", "Size"]
-    wrapped_tree = wrap(model, feature_names = feature_names)
+    features_names = ["Color", "Size"]
+    wrapped_tree = wrap(model, features_names = features_names)
 
     println("--> plot the tree using the `TreeRecipe`")
     plt = plot(wrapped_tree)        # this calls automatically the `TreeRecipe`