input: Write a function to develop a decision tree classifier in Python that predicts a fourth feature from three other features. The function should take a dataset as input and return the trained classifier. If the dataset is empty or has less than 4 features, the function should raise a ValueError.
  assert develop_decision_tree_classifier(pd.read_csv('data.csv')) is not None
  assert develop_decision_tree_classifier(pd.DataFrame())==ValueError
  assert develop_decision_tree_classifier(pd.DataFrame([[1, 2, 3]]))==ValueError
output: def develop_decision_tree_classifier(dataset):
      if dataset.empty or dataset.shape[1] < 4:
          raise ValueError
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
      X = dataset.iloc[:, :-1]
      y = dataset.iloc[:, -1]
      X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
      classifier = DecisionTreeClassifier()
      classifier.fit(X_train, y_train)
      return classifier