code: def test_randomforest_for_classification():
      config = {
          "algorithm": "ID3",
          "enableRandomForest": True,
          "num_of_trees": 3,
      }
      df = pd.read_csv("dataset/car.data")
  
      model = cb.fit(df, config, silent=True)
  
      assert model["config"]["algorithm"] == "ID3"
      assert model["evaluation"]["train"]["Accuracy"] > 90
  
      # feature importance
      decision_rules = []
      for tree in model["trees"]:
          decision_rule = tree.__dict__["__spec__"].origin
          decision_rules.append(decision_rule)
  
      df = cb.feature_importance(decision_rules, silent=True)
      assert df.shape[0] == 6
  
      # this is not in train data
      instance = ["high", "high", 4, "more", "big", "high"]
      prediction = cb.predict(model, instance)
      assert prediction in ["unacc", "acc"]
  
      instance = ["vhigh", "vhigh", 2, "2", "small", "low"]
      prediction = cb.predict(model, instance)
      assert prediction in ["unacc", "acc"]
  
imports: import pandas
  from chefboost import Chefboost
  from chefboost.commons.logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: test_randomforest_for_classification() without return types