input: Write a function to classify an input string using a machine learning classifier implemented in Python. The function should extract features from the input string, train a Naive Bayes classifier, and return the prediction.
  
  def classify_string(input_string):
    features = ...  # Feature extraction and transformation from string to numeric
    model = MultinomialNB()  # Create an instance of the classifier
    model.fit(data, target)  # Train the classifier on the data
    prediction = model.predict([features])  # Make a prediction based on the input
    return prediction  # Return the prediction
  
  assert classify_string("Hello, how are you?") == ['greeting']
  assert classify_string("What is the latest news?") == ['inquiry']
  assert classify_string("Tell me a joke.").sort() == ['command', 'humor']
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      
      data = ["Hello, how are you?", "What is the latest news?", "Tell me a joke."]
      target = ['greeting', 'inquiry', 'humor']
      
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
      
      model = MultinomialNB()
      model.fit(X, target)
      
      features = vectorizer.transform([input_string])
      prediction = model.predict(features)
      
      return prediction