customers' Facebook posts to develop a sentiment prediction model. The model will be used to predict whether customers have a favorable or unfavorable opinion of their new products. A statistical procedure was used to develop the model. Then it was run using some test cases to determine how "good" it was at predicting correctly. The output of the testing procedure includes a confusion matrix. The confusion matrix displays these values: number of false positives number of true positives number of false negatives number of true negatives. Here is the confusion matrix. Use this information to answer the next three questions. Predicted to be Neg Pos 80 20 10 90 Actually Neg Are Pos 100 100 90 110 200 Question 30 (1 point) What is the recall for the Negative outcome class? A) 80 / 110 or 72% B) 90/110 or 82% OC) 90/100 or 90% D) 80/100 or 80% Question 31 (1 point) What is the precision of this model for predicting Positive outcomes? 1) 80 / 110 or 72% 2) 90 / 100 or 90% 3) 90 / 110 or 82% 4) 80 / 100 or 80% What is the overall accuracy of this model? a) 110/200 or 55% b) 100/200 or 50% Oc) 170/200 or 85% d) 80/200 or 40% e) 90/200 or 45% Question 33 (1 point) Grocery and other retail stores may use to identify what combination of products their customers are likely to purchase together when shopping at their stores. 1) customer segmentation 2) classification 3) anomaly detection 4) association rule analysis