code: def viz_knowledge_one_feature(
      orientation="TD", max_depth=3, random_state=666, fancy=True
  ):
      # data from https://archive.ics.uci.edu/ml/datasets/User+Knowledge+Modeling
      clf = tree.DecisionTreeClassifier(
          max_depth=max_depth, random_state=random_state
      )
      know = pd.read_csv("data/knowledge.csv")
      target_names = ["very_low", "Low", "Middle", "High"]
      know["UNS"] = know["UNS"].map(
          {n: i for i, n in enumerate(target_names)}
      )
  
      the_feature = "PEG"
      X_train, y_train = know[[the_feature]], know["UNS"]
      clf.fit(X_train, y_train)
  
      X = X_train.iloc[np.random.randint(0, len(X_train))]
      X = None
  
      viz = dtreeviz(
          clf,
          X_train,
          y_train,
          target_name="UNS",
          feature_names=[the_feature],
          orientation=orientation,
          class_names=target_names,
          show_node_labels=True,
          fancy=fancy,
          X=X,
      )
  
      viz.save(f"{parrt_article}/knowledge-TD-PEG.svg")
  
imports: import numpy
  from animl.trees import *
  from animl.viz.trees import *
label_desc: Use image node_id 2 for calling a global function with example usage: viz_knowledge_one_feature(orientation, max_depth, random_state, fancy) without return types