input: Write a function to develop a decision tree classifier in Python that predicts a fourth feature from three other features using the provided dataset. Return the trained classifier.
  
  
  def create_decision_tree_classifier():
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      # Importing the dataset
      dataset = pd.read_csv('data.csv')
  
      # Splitting the dataset into features and labels
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, 3].values
  
      # Initializing the DecisionTreeClassifier with entropy criterion
      classifier = DecisionTreeClassifier(criterion='entropy')
  
      # Fitting the classifier to the dataset
      classifier.fit(X, y)
  
      return classifier
  
  # Example usage:
  classifier = create_decision_tree_classifier()
  
  
  assert isinstance(create_decision_tree_classifier(), DecisionTreeClassifier)==True
output: def create_decision_tree_classifier():
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      dataset = pd.read_csv('data.csv')
      X = dataset.iloc[:, :-1].values
      y = dataset.iloc[:, 3].values
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
      return classifier