input: Write a function to create a machine learning classifier in Python for categorizing user input using the Multinomial Naive Bayes algorithm. If the input string is not categorized, return False. If the input string is categorized, return True.
  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 != False
  
  assert classify_string("example input") == True
  assert classify_string("another example") == True
  assert classify_string("uncategorized input") == False
output: def classify_string(input_string):
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.naive_bayes import MultinomialNB
    
    # Example data for training
    data = ["example input", "another example"]
    target = [True, True]
    
    # Feature extraction and transformation from string to numeric
    vectorizer = CountVectorizer()
    X_train = vectorizer.fit_transform(data)
    features = vectorizer.transform([input_string])
    
    # Create an instance of the classifier 
    model = MultinomialNB()
    
    # Train the classifier on the data
    model.fit(X_train, target)
    
    # Make a prediction based on the input
    prediction = model.predict(features)
    
    # Return the prediction
    return prediction[0]