input: from typing import List
  
  def classify_string(input_string: str) -> str:
      """
      Create a machine learning classifier in Python for categorizing user input using a Naive Bayes model.
      
      Example:
      >>> classify_string("This is a positive statement.")
      'positive'
      >>> classify_string("This is a negative statement.")
      'negative'
      """
      from sklearn.naive_bayes import MultinomialNB
      
      # 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]
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.datasets import fetch_20newsgroups
  
      # Load a sample dataset for training
      categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']
      twenty_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=42)
  
      # Feature extraction and transformation from string to numeric
      vectorizer = CountVectorizer()
      X_train = vectorizer.fit_transform(twenty_train.data)
      y_train = twenty_train.target
  
      # Create an instance of the classifier 
      model = MultinomialNB()
  
      # Train the classifier on the data
      model.fit(X_train, y_train)
  
      # Transform the input string to numeric features
      features = vectorizer.transform([input_string])
  
      # Make a prediction based on the input
      prediction = model.predict(features)
  
      # Map the prediction to a category name
      category_names = ['negative', 'positive']
      return category_names[prediction[0]]