input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features, and then use the classifier to predict the output for a new set of features. The function should return the predicted value.
  
  
  def decision_tree_predictor(new_data):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
  
      # Assume 'data.csv' contains the dataset with features A, B, C, and the target feature
      dataset = pd.read_csv('data.csv')
      
      # Splitting the dataset into features and labels
      X = dataset.iloc[:, :-1].values 
      y = dataset.iloc[:, 3].values
  
      # Initializing and fitting the model
      classifier = DecisionTreeClassifier(criterion='entropy')
      classifier.fit(X, y)
  
      # Predicting the output for new data
      prediction = classifier.predict([new_data])
      return prediction[0]
  
  assert decision_tree_predictor([0.5, 0.3, 0.2]) == 0 
  assert decision_tree_predictor([0.9, 0.7, 0.1]) == 1 
  assert decision_tree_predictor([0.1, 0.8, 0.4]) == 0 
  assert decision_tree_predictor([0.3, 0.4, 0.6]) == 1 
  
output: def decision_tree_predictor(new_data):
      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)
  
      prediction = classifier.predict([new_data])
      return prediction[0]