input: Write a function to create a machine learning classifier in Python for categorizing user input using the Multinomial Naive Bayes algorithm. The function should take an input string, extract features, train a classifier on the data, and return a prediction. If no input string is provided, the function should raise a ValueError.
  def classify_string(input_string):
    if not input_string:
      raise ValueError("Input string must not be empty.")
    
    # 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("User input string here")==[prediction_value]
  assert classify_string("")
  assert classify_string("Another user input string")==[another_prediction_value]
  assert classify_string("Yet another input string")==[yet_another_prediction_value]
  assert classify_string("This is a test input string")==[test_prediction_value]
output: def classify_string(input_string):
    if not input_string:
      raise ValueError("Input string must not be empty.")
    
    # Feature extraction and transformation from string to numeric
    vectorizer = CountVectorizer()
    data = vectorizer.fit_transform(["example training data"])
    target = [0]  # Example target values
    
    features = vectorizer.transform([input_string])
    
    # 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