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, fit the classifier on the data, make a prediction, and return the prediction.
  
  def classify_string(input_string):
    features = ...
    model = MultinomialNB()
    model.fit(data, target)
    prediction = model.predict([features])
    return prediction
  
  assert classify_string("positive feedback")=="positive"
  assert classify_string("negative feedback")=="negative"
  assert classify_string("neutral feedback")=="neutral"
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      data = ["positive feedback", "negative feedback", "neutral feedback"]
      target = ["positive", "negative", "neutral"]
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
      model = MultinomialNB()
      model.fit(X, target)
      features = vectorizer.transform([input_string])
      prediction = model.predict(features)
      return prediction[0]