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What type of epithelium appears stratified, but is not?

Problem 1 (24 points) True/False, please explain why. 

Problem 5: (14 points) Clustering Algorithm Analysis 1) (5 Points) Given a scenario, K-means cluster may NOT work very well (2 points). Explain why (3 points). 2) (5 Points) Given a scenario, DBSCAN algorithm could NOT work very well (2 points). Explain why (3 points). 3) (4 Points) How can we improve the random initialization of classic K-means algorithm?

K-means is more resistant to noise than DBSCAN. 

ALL students enrolled in this course must complete ________ field experience hours during the current semester at a traditional public or charter school site to satisfy the requirements for this course.  

Problem 6 (22 points) Information Gain and Split Plans Consider the following data set for a binary class problem.  Illustrate your work/math to calculate the classification error rate when splitting on A and B. Which attribute would the decision tree induction algorithm choose? The definition of misclassification error is:  1) (5 Points) The overall misclassification error before splitting: 2) (5 Points) The gain in misclassification error after splitting on A: 3) (5 Points) The gain in misclassification error after splitting on B: 4) (3 Points) Which attribute would the decision tree choose: 5) (4 Points) There are three impurity measurements: entropy, misclassification error, and Gini index. Which one is the best for measuring impurity, and why?

Problem 2: (14 points) Types of Attributes Classify the following attributes as nominal, ordinal, interval, ratio. There is no need to explain why. 

Which of the following would best describe the characteristics of a muscle that is assigned a grade of trace (1)?

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KNN is more resistant to noise than Decision Tree