input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. The function should return the fitted classifier. The input is a dataset with three features (A, B, C) and a fourth feature to predict.
  assert type(decision_tree_classifier(pd.read_csv('data.csv'))).__name__ == 'DecisionTreeClassifier'
  assert decision_tree_classifier(pd.read_csv('data.csv')).criterion == 'entropy'
  assert decision_tree_classifier(pd.read_csv('data.csv')).n_features_in_ == 3
output: def decision_tree_classifier(data):
      from sklearn.tree import DecisionTreeClassifier
      X = data[['A', 'B', 'C']]
      y = data.iloc[:, -1]
      clf = DecisionTreeClassifier(criterion='entropy')
      clf.fit(X, y)
      return clf