input: Write a function to create a machine learning classifier in Python that categorizes user input using the Multinomial Naive Bayes algorithm. The function should take an input string, extract features, and return the predicted category. If the input string is empty, return an empty string as the prediction.
  
  def classify_string(input_string):
    if not input_string:
      return ""
  
    # 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("positive feedback")=="positive"
  assert classify_string("bad experience")=="negative"
  assert classify_string("neutral comment")=="neutral"
  assert classify_string("")==""
output: def classify_string(input_string):
    if not input_string:
      return ""
  
    # Feature extraction and transformation from string to numeric
    features = [len(input_string)]
  
    # Create an instance of the classifier 
    model = MultinomialNB()
  
    # Train the classifier on the data
    data = [[10], [3], [5]]
    target = ["positive", "negative", "neutral"]
    model.fit(data, target)
  
    # Make a prediction based on the input
    prediction = model.predict([features])
  
    # Return the prediction
    return prediction[0]