input: Write a function to create and fit a decision tree classifier using Python's scikit-learn library to predict the fourth feature from three other features in a given dataset. The function should load the dataset from a CSV file, split the dataset into features and labels, and then fit the classifier to the data. The function should return the trained classifier.
  
  assert create_decision_tree_classifier('data.csv').criterion == 'entropy'
output: def create_decision_tree_classifier(file_path):
      import pandas as pd
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.model_selection import train_test_split
      
      data = pd.read_csv(file_path)
      X = data.iloc[:, :3]
      y = data.iloc[:, 3]
      
      clf = DecisionTreeClassifier(criterion='entropy')
      clf.fit(X, y)
      
      return clf