input: from sklearn.naive_bayes import MultinomialNB
  
  def classify_string(input_string):
      """ 
      Creates a machine learning classifier to categorize user input using the Multinomial Naive Bayes algorithm.
      
      >>> classify_string("Hello, I am happy today")
      ['positive']
      
      >>> classify_string("I feel very sad")
      ['negative']
      """
output:     from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.pipeline import Pipeline
      from sklearn.preprocessing import LabelEncoder
  
      # Sample data for training
      data = [
          ("Hello, I am happy today", "positive"),
          ("I feel very sad", "negative"),
          ("This is a great day", "positive"),
          ("I am feeling down", "negative"),
      ]
  
      # Split data into features and labels
      X_train = [x[0] for x in data]
      y_train = [x[1] for x in data]
  
      # Create a pipeline with CountVectorizer and MultinomialNB
      model = Pipeline([
          ('vectorizer', CountVectorizer()),
          ('classifier', MultinomialNB())
      ])
  
      # Train the model
      model.fit(X_train, y_train)
  
      # Predict the category of the input string
      prediction = model.predict([input_string])
  
      return prediction.tolist()