input: Please provide the Python code for a machine learning classifier that categorizes user input using a specified model.
  
  ### Python Code:
  
  from sklearn.naive_bayes import MultinomialNB 
  
  # Assuming an input string is passed to the function as a parameter
  
  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
  
output:     from sklearn.feature_extraction.text import CountVectorizer
      import numpy as np
  
      # Example data for training
      data = ["This is a positive review", "This is a negative review", "Another positive review"]
      target = np.array([1, 0, 1])  # 1 for positive, 0 for negative
  
      # Feature extraction and transformation from string to numeric
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
  
      # Transform the input string
      features = vectorizer.transform([input_string])
  
      # Create an instance of the classifier 
      model = MultinomialNB()
  
      # Train the classifier on the data
      model.fit(X, target)
  
      # Make a prediction based on the input
      prediction = model.predict(features)
  
      # Return the prediction
      return prediction[0]