input: Write a function to create a machine learning classifier in Python that categorizes user input using a Multinomial Naive Bayes model. The function should take an input string, extract and transform features from the string to numeric values, train the classifier on some data, and then return the prediction based on the input string.
  
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_string(input_string):
    # Feature extraction and transformation from string to numeric
    features = ...
    # Create an instance of the classifier
    model = MultinomialNB()
    # Train the classifier on the data
    model.fit(data, target)
    # Make a prediction based on the input
    prediction = model.predict([features])
    # Return the prediction
    return prediction
  
  assert classify_string("a") == [0]
  assert classify_string("b") == [1]
  assert classify_string("c") == [2]
  assert classify_string("d") == [3]
  assert classify_string("e") == [4]
output: 
  def classify_string(input_string):
    features = [ord(char) - ord('a') for char in input_string]
    data = [[0], [1], [2], [3], [4]]
    target = [0, 1, 2, 3, 4]
    model = MultinomialNB()
    model.fit(data, target)
    prediction = model.predict([features])
    return prediction
  