input: 
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_string(input_string: str) -> str:
      """ Create a machine learning classifier in Python for categorizing user input.
      The function takes a string input, extracts features, trains a MultinomialNB 
      classifier, and returns the predicted category of the input string.
      
      >>> classify_string("Hello, how are you?")
      "greeting"
      >>> classify_string("What is the weather today?")
      "weather"
      """
      # Example implementation:
      # features = some_feature_extraction_function(input_string)
      # model = MultinomialNB()
      # model.fit(training_data, training_labels)
      # prediction = model.predict([features])
      # return prediction[0]
      pass
  
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
      import numpy as np
  
      # Example training data
      training_data = ["Hello, how are you?", "What is the weather today?", "Good morning!", "How's the weather?", "Hi there!", "What's up?"]
      training_labels = ["greeting", "weather", "greeting", "weather", "greeting", "greeting"]
  
      # Feature extraction
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(training_data)
  
      # Train the model
      model = MultinomialNB()
      model.fit(X, training_labels)
  
      # Extract features from input string
      input_features = vectorizer.transform([input_string])
  
      # Predict the category
      prediction = model.predict(input_features)
      return prediction[0]