MLJ provides two other DecisionTree model types, which you can load with
Tree1 = @load DecisionTreeClassifier pkg=DecisionTree
Tree2 = @load DecisionTreeClassifier pkg=ScikitLearn
You can use them assuming your features are all Continuous (OrderedFactor also okay for the first one). I don’t think missings are supported.
Do they give very different results? (Take some care, as the default hyperparameters may be different).