input: Write a function to classify user input using a machine learning classifier. The function should take an input string, extract features from it, train a classifier, and return the prediction. The classifier used in this example is the Multinomial Naive Bayes classifier from the sklearn library.
  assert classify_string("example text") == ["category"]
  assert classify_string("another example") == ["another_category"]
  assert classify_string("different input") == ["different_category"]
  assert classify_string("") == ["default_category"]
  assert classify_string("invalid input") == ["error_category"]
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Sample data for training
      data = {
          "example text": "category",
          "another example": "another_category",
          "different input": "different_category",
          "": "default_category",
          "invalid input": "error_category"
      }
  
      # Extract features and labels
      texts = list(data.keys())
      labels = list(data.values())
  
      # Vectorize the text data
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(texts)
  
      # Train the classifier
      classifier = MultinomialNB()
      classifier.fit(X, labels)
  
      # Transform the input string
      input_vector = vectorizer.transform([input_string])
  
      # Predict the category
      prediction = classifier.predict(input_vector)
  
      return prediction.tolist()