You are approached by the marketing director of a local company, who believes that he has devised a foolproof way to measure customer satisfaction.He explains his scheme as follows: “It’s so simple that I can’t believe that no one has thought of it before. I just keep track of the number of customer complaints about each product. I read in a data mining book that counts are ratio attributes, and so, my measure of product satisfaction must be a ratio attribute. But when I rated the products based on my new customer satisfaction measure and showed them to my boss, he told me that I had overlooked the obvious and that my measure was worthless. I think that he was just mad because our best-selling product had the worst satisfaction since it had the most complaints. Could you help me set him straight?”   Who is right, the marketing director or his boss? If you answered, his boss, what would you do to fix the measure of satisfaction?  

(a) (4) What is meant when we say that a classifier has done underfitting?   (b) (4) How would you ensure that a decision tree you learn avoids underfitting?   (c) (4) How would you ensure that a linear SVM that you learn avoids underoverfitting?

Answer the following in the context of the Adaboost algorithm. (No formulas, only language description). (a) (4) Which points are given higher/lower weights after learning each weak-classifier?   (b) (4) How is the weight assigned to each data point used by the algorithm?   (c) (4) How do we assign weights to weak classifiers for their contribution in the global decision?

10) Consider the context of selecting the best attribute for decision tree construction. Explain briefly the difference between “information gain” and “gain ratio” metrics for selecting the best attributes.  Is any one of these two better than the other – explain why? Do not write any formulas. Explain in words only.

(10) In what situation will you prefer a decision tree model that has a high recall value? Why so?

Gas exchange occurs in the

Consider the following data points and their class labels: [P1: (1, 3, 4), A], [P2: (7, 2, 4), B], [P3: (8, 1, 6), B], [P4: (7, 7, 1), A], [P5, (2, 1, 1), A]. We want to use the distance weighted 3-NN classification with this data.  Each point at a distance of d has a vote of (1/d) for predicting thee class label.   (a) (8) For the query point (4, 4, 4) find and report the data points in its 3-nearest-neighbor list, their weights for decision, and the class label that should be assigned to this query point.     (b) (6) What is gained and what is lost when we increase the value of K in a K-NN classifier?

Answer the following in the context of the Adaboost algorithm. (No formulas, only language description). (a) (4) Which points are given higher/lower weights after learning each weak-classifier?   (b) (4) How is the weight for each data point used by the algorithm?   (c) (4) How do we assign weights to weak classifiers for their contribution to the global decision?

Consider the following three data vectors. D1: (4, 6, 7, 9), D2:(6, 9, 10, 14), and D3: (4, 6, 2, 1). (a) (3) What are the Manhattan distances for the data-point pairs: D1-D2: D1-D3: D2-D3: (b) (5) What are the cosine similarities for the data-point pairs: D1-D2: D1-D3: D2-D3: (c) (2) Which two pints are the closest as per the Manhattan distance?   (d) (2) Which two points are the closest as per the Cosine similarity?

List and describe five laryngeal visualization methods.