input: Write a function to classify an input string using a machine learning classifier in Python. The classifier should be based on the Multinomial Naive Bayes algorithm. The function should extract features from the input string, train the model on given data and target, and then predict the category of the input string. If the input string is classified as "spam", return True, otherwise return False.
  assert classify_string("Hello, please check out this amazing offer!")==True
  assert classify_string("Hi there, how are you?")==False
  assert classify_string("Buy now and get 50% off!")==True
  assert classify_string("Can we schedule a meeting for tomorrow?")==False
  assert classify_string("")==False
output: def classify_string(input_string):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      data = ["Hello, please check out this amazing offer!",
              "Hi there, how are you?",
              "Buy now and get 50% off!",
              "Can we schedule a meeting for tomorrow?"]
      target = [True, False, True, False]
  
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
  
      clf = MultinomialNB()
      clf.fit(X, target)
  
      input_vector = vectorizer.transform([input_string])
      prediction = clf.predict(input_vector)
  
      return prediction[0]