input: def classify_string(input_string):
      """
      This function takes a user input string, extracts features, trains a Naive Bayes classifier,
      and returns the category prediction.
      >>> classify_string("This is a positive review")
      'Positive'
      >>> classify_string("This is a negative comment")
      'Negative'
      >>> classify_string("This is neutral feedback")
      'Neutral'
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Sample data for training
      data = [
          ("This is a positive review", "Positive"),
          ("This is a negative comment", "Negative"),
          ("This is neutral feedback", "Neutral")
      ]
  
      # Extract features and labels
      texts, labels = zip(*data)
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
  
      # Train the classifier
      classifier = MultinomialNB()
      classifier.fit(X, labels)
  
      # Transform the input string
      input_vector = vectorizer.transform([input_string])
  
      # Predict the category
      prediction = classifier.predict(input_vector)
  
      return prediction[0]