input: Write a function to create 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 for the input.
  
  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") in ["category1", "category2"]
  assert classify_string("another example") in ["category1", "category2"]
  assert classify_string("yet another input") in ["category1", "category2"]
output: def classify_string(input_string):
    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.naive_bayes import MultinomialNB
    data = ["example input", "another example", "yet another input"]
    target = ["category1", "category2", "category1"]
    vectorizer = CountVectorizer()
    X = vectorizer.fit_transform(data)
    features = vectorizer.transform([input_string])
    model = MultinomialNB()
    model.fit(X, target)
    prediction = model.predict(features)
    return prediction[0]