input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. The function should import the dataset from 'data.csv', split it into features and labels, and then fit the classifier to the dataset using 'entropy' as the criterion. The function should return the fitted classifier.
  
  
  def decision_tree():
      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
      
      # creating the classifier 
      classifier = DecisionTreeClassifier(criterion = 'entropy')
      
      # fitting the classifier to the dataset
      classifier.fit(X, y)
      
      return classifier
  
  # Example test case:
  # Assuming 'data.csv' contains the appropriate data for features A, B, C, and the fourth feature.
  assert isinstance(decision_tree(), DecisionTreeClassifier)
output: def decision_tree():
      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