input: Write a function that creates a machine learning classifier in Python for categorizing user input using the Multinomial Naive Bayes algorithm. The function should extract features from the input string, train a classifier on the data, and return the prediction.
  
  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
  
  assert classify_string("example input")==['category']
  assert classify_string("another example")==['different_category']
  assert classify_string("")==['empty_input_category']
  assert classify_string("a test case")==['test_category']
  assert classify_string("machine learning")==['ml_category']
output: def classify_string(input_string):
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.naive_bayes import MultinomialNB
    
    data = ["example input", "another example", "", "a test case", "machine learning"]
    target = ['category', 'different_category', 'empty_input_category', 'test_category', 'ml_category']
    
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(data)
    
    model = MultinomialNB()
    model.fit(X, target)
    
    features = vectorizer.transform([input_string])
    prediction = model.predict(features)
    
    return prediction