input: Write a function to create a machine learning classifier in Python for categorizing user input using the MultinomialNB algorithm from scikit-learn. The function should take an input string, perform feature extraction to transform the string into a numeric format, train the classifier, and return the predicted category. 
  
  
  def classify_string(input_string):
  
    # Feature extraction and transformation from string to numeric
    features = . . .
  
    # Create an instance of the classifier 
    model = MultinomialNB()
  
    # Train the classifier on the data
    model.fit(data, target)
  
    # Make a prediction based on the input
    prediction = model.predict([features])
  
    # Return the prediction
    return prediction
  
  
  ### Test Cases:
  
  assert classify_string("Hello, how are you?") in ["greeting", "inquiry"]
  assert classify_string("What is the weather today?") in ["weather", "inquiry"]
  assert classify_string("Schedule a meeting for tomorrow.") in ["meeting", "action"]
  assert classify_string("Buy groceries online.") in ["shopping", "action"]
  assert classify_string("") == "unknown"
  
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 weather today?", "Schedule a meeting for tomorrow.", "Buy groceries online."]
    target = ["greeting", "weather", "meeting", "shopping"]
  
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(data)
  
    model = MultinomialNB()
    model.fit(X, target)
  
    if input_string == "":
      return "unknown"
  
    features = vectorizer.transform([input_string])
    prediction = model.predict(features)
  
    return prediction[0]