input: Write a function that creates a machine learning classifier in Python for categorizing user input using the Multinomial Naive Bayes algorithm. The function should take an input string, extract and transform features from this string, train the classifier on the provided data, and return a 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("positive sentiment") == ["positive"]
  assert classify_string("negative sentiment") == ["negative"]
  assert classify_string("neutral sentiment") == ["neutral"]
  assert classify_string("extremely positive") == ["positive"]
  assert classify_string("very negative") == ["negative"]
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      data = ["positive sentiment", "negative sentiment", "neutral sentiment", "extremely positive", "very negative"]
      target = ["positive", "negative", "neutral", "positive", "negative"]
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
      features = vectorizer.transform([input_string])
      model = MultinomialNB()
      model.fit(X, target)
      prediction = model.predict(features)
      return prediction