diff --git a/src/MLJDecisionTreeInterface.jl b/src/MLJDecisionTreeInterface.jl
index 281b58d..86c5626 100644
--- a/src/MLJDecisionTreeInterface.jl
+++ b/src/MLJDecisionTreeInterface.jl
@@ -79,7 +79,7 @@ end
 
 function get_encoding(classes_seen)
     a_cat_element = classes_seen[1]
-    return Dict(c => MMI.int(c) for c in MMI.classes(a_cat_element))
+    return Dict(MMI.int(c) => c for c in MMI.classes(a_cat_element))
 end
 
 MMI.fitted_params(::DecisionTreeClassifier, fitresult) =
@@ -538,9 +538,9 @@ To interpret the internal class labelling:
 ```
 julia> fitted_params(mach).encoding
 Dict{CategoricalArrays.CategoricalValue{String, UInt32}, UInt32} with 3 entries:
-  "virginica"  => 0x00000003
-  "setosa"     => 0x00000001
-  "versicolor" => 0x00000002
+  0x00000003 => "virginica"
+  0x00000001 => "setosa"
+  0x00000002 => "versicolor"
 ```
 
 See also
diff --git a/test/runtests.jl b/test/runtests.jl
index 14553f2..ad97520 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -49,6 +49,9 @@ yyhat = predict_mode(baretree, fitresult, MLJBase.selectrows(X, 1:3))
 fp = fitted_params(baretree, fitresult)
 @test Set([:tree, :encoding, :features]) == Set(keys(fp))
 @test fp.features == report.features
+enc = fp.encoding
+@test Set(values(enc)) == Set(["virginica", "setosa", "versicolor"])
+@test enc[MLJBase.int(y[end])] == "virginica"
 
 using Random: seed!
 seed!(0)