input: 
  from sklearn.naive_bayes import MultinomialNB 
  
  def classify_string(input_string):
      """ Create a machine learning classifier for categorizing user input.
      >>> classify_string("This is a positive review")
      'positive'
      >>> classify_string("This is a negative review")
      'negative'
      """
  
      # 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 dataset
      categories = ['alt.atheism', 'comp.graphics', 'sci.space', 'talk.religion.misc']
      newsgroups_train = fetch_20newsgroups(subset='train', categories=categories)
  
      # Feature extraction and transformation from string to numeric
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(newsgroups_train.data)
      y = newsgroups_train.target
  
      # Split data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, 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
      features = vectorizer.transform([input_string])
  
      # Make a prediction based on the input
      prediction = model.predict(features)
  
      # Return the prediction
      return newsgroups_train.target_names[prediction[0]]