[88] \# Examine the evaluation results on testing data: accuracy, precision, recall, and f1-score (1 point) print(classification_report(target_test, prediction_on_test)) Q6. Assume the following costs/benefits: - Cost of marketing to a customer: $30 per customer receiving marketing. - Average Bank income for a purchase: $200 per customer that purchases term deposit. - Opportunity cost of person not marketed but who would have been a purchaser: $40 Based on the above costs / benefits, what is the total net benefit / cost of the Naive Bayes model on testing data? (3 point) Q7. Based on the calculated net benefit/cost, which model (Decision Tree or Naive Bayes) should be adopted for marketing campaign? why? (2 point) Q8. Compare the performances (accuracy, precision, recall, and F-measure) of Decision Tree and Naive Bayes model, and answer the following questions 8.1. Which model has better overall performance on testing data? ( 1 point) 8.2. Which model has better performance on the "yes" class? (1 point) 8.3. Which model has better performance on the "no" class? (1 point) 8.4. Which model can identify more customers purchasing term deposit? (1 point) 8.5. Interpret the precision value on the "yes" class for both models (1 point)