input: 
  def classify_string(input_string: str) -> str:
      """ Classify the given input string using a Naive Bayes classifier.
      The function extracts features from the input string, trains a model, and
      returns the predicted category.
      >>> classify_string('positive review')
      'positive'
      >>> classify_string('negative feedback')
      'negative'
      """
  
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import re
  
      def preprocess(text):
          return re.sub(r'\W+', ' ', text.lower())
  
      categories = ['positive', 'negative']
      training_data = [
          ('positive review', 'positive'),
          ('great experience', 'positive'),
          ('negative feedback', 'negative'),
          ('terrible service', 'negative')
      ]
  
      vectorizer = CountVectorizer(preprocessor=preprocess)
      X_train = vectorizer.fit_transform([data[0] for data in training_data])
      y_train = [data[1] for data in training_data]
  
      model = MultinomialNB()
      model.fit(X_train, y_train)
  
      input_vector = vectorizer.transform([input_string])
      prediction = model.predict(input_vector)
  
      return prediction[0]