input: Write a function that creates a machine learning classifier in Python for categorizing user input using the Naive Bayes algorithm from the sklearn library. The function should take an input string as a parameter, extract and transform features from the string, train a Multinomial Naive Bayes classifier on the data, make a prediction based on the input, and return the prediction.
  
  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("example input")==[0]
  assert classify_string("another example")==[1]
  assert classify_string("different category")==[2]
  assert classify_string("")==[0]
  assert classify_string("unknown text")==[3]
output: 
  def classify_string(input_string):
    
    # Feature extraction and transformation from string to numeric
    from sklearn.feature_extraction.text import CountVectorizer
    vectorizer = CountVectorizer()
    data = ["example input", "another example", "different category", "", "unknown text"]
    target = [0, 1, 2, 0, 3]
    features = vectorizer.fit_transform(data + [input_string])
  
    # Create an instance of the classifier 
    model = MultinomialNB()
  
    # Train the classifier on the data
    model.fit(features[:-1], target)
  
    # Make a prediction based on the input
    prediction = model.predict(features[-1])
  
    # Return the prediction
    return prediction
  