input: Write a function to categorize user input using a machine learning classifier in Python. The function should transform the input string into numeric features and predict its category using a Multinomial Naive Bayes classifier. If an input string is given as a parameter to the function, it should return the predicted category.
  def classify_string(input_string):
      features = ...  # Feature extraction and transformation from string to numeric
      model = MultinomialNB()  # Create an instance of the classifier
      model.fit(data, target)  # Train the classifier on the data
      prediction = model.predict([features])  # Make a prediction based on the input
      return prediction  # Return the prediction
  
  assert classify_string("This is a positive sentence")=="positive"
  assert classify_string("This is a negative statement")=="negative"
  assert classify_string("A neutral comment")=="neutral"
  assert classify_string("This is somewhat positive")=="positive"
  assert classify_string("This is definitely negative")=="negative"
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import joblib
      
      vectorizer = joblib.load('vectorizer.pkl')
      model = joblib.load('model.pkl')
      
      features = vectorizer.transform([input_string])
      prediction = model.predict(features)
      return prediction[0]