input: Write a function to develop a decision tree classifier in Python to predict a fourth feature from three other features. The function should read a dataset from a CSV file, split it into features and labels, train a decision tree classifier with entropy as the criterion, and fit the classifier to the dataset. If the training is successful, return True; otherwise, return False.
  
  assert decision_tree_classifier('data.csv')==True
output: def decision_tree_classifier(file_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
  
      data = pd.read_csv(file_path)
      X = data.iloc[:, :-1]
      y = data.iloc[:, -1]
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      clf = DecisionTreeClassifier(criterion='entropy')
      clf.fit(X_train, y_train)
  
      y_pred = clf.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return accuracy > 0