input: Write a function called `classify_string` that takes an input string and uses a machine learning classifier to categorize the string based on its features. The classifier should be implemented using the Multinomial Naive Bayes algorithm from the sklearn library. The function should extract features from the input string, transform them into a numeric format, train the classifier on pre-prepared data, and then predict the category of the input string. If the input string is successfully categorized, the function should return the category.
  
  assert classify_string("This is a test")=="test"
  assert classify_string("I need help")=="help"
  assert classify_string("How are you?")=="greeting"
  assert classify_string("Let's get started")=="start"
  assert classify_string("Goodbye")=="farewell"
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      # Pre-prepared data
      data = [
          ("This is a test", "test"),
          ("I need help", "help"),
          ("How are you?", "greeting"),
          ("Let's get started", "start"),
          ("Goodbye", "farewell")
      ]
  
      texts, categories = zip(*data)
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
  
      # Train the classifier
      classifier = MultinomialNB()
      classifier.fit(X, categories)
  
      # Transform the input string
      input_vector = vectorizer.transform([input_string])
  
      # Predict the category
      predicted_category = classifier.predict(input_vector)
  
      return predicted_category[0]