input: Write a function to classify user input using a machine learning classifier. The classifier should use the Multinomial Naive Bayes algorithm from scikit-learn. The function should take an input string, extract and transform its features, train the classifier with a pre-defined dataset, and then predict the category of 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("This is a positive review")=="Positive"
  assert classify_string("This is a negative review")=="Negative"
  assert classify_string("This is a neutral comment")=="Neutral"
  assert classify_string("")==None
output: 
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_string(input_string):
    data = ["This is a positive review", "This is a negative review", "This is a neutral comment"]
    target = ["Positive", "Negative", "Neutral"]
    
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(data)
    
    model = MultinomialNB()
    model.fit(X, target)
    
    if input_string == "":
      return None
    
    features = vectorizer.transform([input_string])
    prediction = model.predict(features)
    
    return prediction[0]
  