input: from typing import List
  
  def categorize_user_input(input_string: str) -> str:
      """Creates a machine learning classifier in Python for categorizing user input.
      The function takes an input string and returns the category predicted by the model.
      >>> categorize_user_input("This is a sample input string.")
      'SampleCategory'
      >>> categorize_user_input("Another example of user input.")
      'ExampleCategory'
      """
      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
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      from sklearn.datasets import fetch_20newsgroups
  
      # Load sample data for training
      newsgroups = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))
      data = newsgroups.data
      target = newsgroups.target
  
      # Feature extraction and transformation from string to numeric
      vectorizer = CountVectorizer()
      features = vectorizer.fit_transform(data)
  
      # Split data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.33, random_state=42)
  
      # Create an instance of the classifier
      model = MultinomialNB()
  
      # Train the classifier on the data
      model.fit(X_train, y_train)
  
      # Transform input string to numeric features
      input_features = vectorizer.transform([input_string])
  
      # Make a prediction based on the input
      prediction = model.predict(input_features)
  
      # Return the category name
      return newsgroups.target_names[prediction[0]]