input: 
  from sklearn.naive_bayes import MultinomialNB 
  
  # Assuming an input string is passed to the function as a parameter
  
  def classify_string(input_string):
      """
      Categorizes the input string using a trained Naive Bayes classifier.
      
      >>> classify_string("example string")
      'category'
      >>> classify_string("another example")
      'another_category'
      """
      
      # 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 = ["example string", "another example", "yet another example"]
      target = ["category", "another_category", "another_category"]
  
      # Feature extraction and transformation from string to numeric
      vectorizer = CountVectorizer()
      features = vectorizer.fit_transform([input_string]).toarray()
  
      # Create an instance of the classifier 
      model = MultinomialNB()
  
      # Train the classifier on the data
      model.fit(vectorizer.transform(data).toarray(), np.array(target))
  
      # Make a prediction based on the input
      prediction = model.predict(features)
  
      # Return the prediction
      return prediction[0]