input: Write a function to classify user input using a machine learning classifier in Python. The function should take an input string, transform it into numeric features, train a MultinomialNB classifier on the data, and return the prediction.
  assert classify_string("Great product, highly recommended!")==["positive"]
  assert classify_string("The product arrived broken and I am very disappointed.")==["negative"]
  assert classify_string("The service was good and the staff were friendly.")==["positive"]
  assert classify_string("")==["neutral"]
  assert classify_string("This is an unknown input")==["unknown"]
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
  
      # Sample data
      data = [
          ("Great product, highly recommended!", "positive"),
          ("The product arrived broken and I am very disappointed.", "negative"),
          ("The service was good and the staff were friendly.", "positive"),
          ("", "neutral"),
          ("This is an unknown input", "unknown")
      ]
  
      # Separate the data into texts and labels
      texts, labels = zip(*data)
  
      # Transform texts into numeric features
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
  
      # Train the classifier
      classifier = MultinomialNB()
      classifier.fit(X, labels)
  
      # Transform the input string into numeric features
      input_features = vectorizer.transform([input_string])
  
      # Predict the label for the input string
      prediction = classifier.predict(input_features)
  
      return list(prediction)