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 first extract features from the input string and then train a classifier on some pre-labeled data. After training, it should predict the category of a new input string.
  
  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 comment")==['positive']
  assert classify_string("This is a negative comment")==['negative']
  assert classify_string("This is a neutral comment")==['neutral']
  assert classify_string("Need more information")==['unclear']
  assert classify_string("")==['no_data']
output: def classify_string(input_string):
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.naive_bayes import MultinomialNB
    
    # Pre-labeled data
    data = ["This is a positive comment", "This is a negative comment", "This is a neutral comment", "Need more information", ""]
    target = ['positive', 'negative', 'neutral', 'unclear', 'no_data']
    
    # Feature extraction and transformation from string to numeric
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(data)
    features = vectorizer.transform([input_string])
    
    # Create an instance of the classifier 
    model = MultinomialNB()
    
    # Train the classifier on the data
    model.fit(X, target)
    
    # Make a prediction based on the input
    prediction = model.predict(features)
    
    # Return the prediction
    return prediction