input: Write a function to implement a machine learning classifier in Python using the Naive Bayes algorithm for categorizing user input strings. The function should take an input string, extract and transform its features into numeric form, train a classifier on provided data, and return the prediction.
  assert classify_string("This is a positive review") == ["Positive"]
  assert classify_string("This is a negative review") == ["Negative"]
  assert classify_string("This is a neutral statement") == ["Neutral"]
  assert classify_string("A highly enthusiastic comment") == ["Positive"]
  assert classify_string("") == ["Unknown"]
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      data = [
          ("This is a positive review", "Positive"),
          ("This is a negative review", "Negative"),
          ("This is a neutral statement", "Neutral"),
          ("A highly enthusiastic comment", "Positive")
      ]
  
      texts, labels = zip(*data)
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
      clf = MultinomialNB()
      clf.fit(X, labels)
  
      if input_string == "":
          return ["Unknown"]
  
      input_vector = vectorizer.transform([input_string])
      prediction = clf.predict(input_vector)
      return prediction.tolist()