You have a dataset that involves three features. Feature in [0, 1000]. The other C’s values are two features are Boolean-valued. A B C Category F T 115 false Ex1 T F 890 false Ex2 T T 257 true Ex3 F F 509 true Ex4 T T 753 true Ex5 How much information about the category is gained by knowing whether or not the value of feature is less than 333? C How much information is there in knowing whether or not features and have the same A B value? A knowledgeable reviewer says that the above data set was not very well pre-processed for nearest-neighbor algorithms. Briefly explain why a reviewer might say that. Assume a one-norm SVM puts weight = -3 on feature weight = 2 on feature and A, B, weight = 0 on feature What would the cost of this solution be, based on this C. question’s five training examples? If you need to make any additional assumptions, be sure to state and briefly justify them.
You have a dataset that involves three features. Feature C’s values are in [0, 1000]. The other two features are Boolean-valued. A B C Category Ex1 F T 115 false Ex2 T F 890 false Ex3 T T 257 true Ex4 F F 509 true Ex5 T T 753 true How much information about the category is gained by knowing whether or not the value of feature C is less than 333? How much information is there in knowing whether or not features A and B have the same value? A knowledgeable reviewer says that the above data set was not very well pre-processed for nearest-neighbor algorithms. Briefly explain why a reviewer might say that. Assume a one-norm SVM puts weight = -3 on feature A, weight = 2 on feature B, and weight = 0 on feature C. What would the cost of this solution be, based on this question’s five training examples? If you need to make any additional assumptions, be sure to state and briefly justify them.
examSpring10.docx