input: Write a function to classify a user input string using a machine learning classifier. If the input string is categorized, return the predicted category. If the input string cannot be categorized, return "Uncategorized". The function should use a Multinomial Naive Bayes classifier from the sklearn library.
  
  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[0] if prediction[0] in target else "Uncategorized"
  
  assert classify_string("greeting")=="greeting"
  assert classify_string("facebook")=="social media"
  assert classify_string("dog")=="animal"
  assert classify_string("abcde")=="Uncategorized"
  assert classify_string("")=="Uncategorized"
output: def classify_string(input_string):
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.naive_bayes import MultinomialNB
  
    data = ["greeting", "facebook", "dog"]
    target = ["greeting", "social media", "animal"]
  
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(data)
  
    model = MultinomialNB()
    model.fit(X, target)
  
    features = vectorizer.transform([input_string])
    prediction = model.predict(features)
  
    return prediction[0] if prediction[0] in target else "Uncategorized"