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Chapter quizzes from class.
Chapter 7
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If predictors are very different in scales, should we standardize predictors before running kNN? Yes
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Consider a kNN model with k=7. For a new observation, there are 5 nearest neighbors in class 1 and 2 nearest neighbors in class 0. With a cutoff = 0.75, which class should we assign this observation to? Class 0. C1 = 5, C0 = 2. C1 = (5/7 = 0.71). C2 =(2/7 = 0.28). Y=0 -> (p (0.71) < cutoff(0.75)) ---> Class 0
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Which of the following weights are reasonable for kNN of prediction? Denote by di the distance of the i-th nearest neighbor. (1/di) / (1/d1 + 1/d2 +... + 1/dk)
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Consider the same 2-class classification problem and use the same output table as in the previous question. Which value for k is most appropriate? 3
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Validation Error with Different K Values
Continuous: You and XLMiners will pick the LOWEST one.
Binary: Pick the lowest non-even K
Probabilitiy new observation belongs to class A
In kNN with k = 3, what is the probability that the new observation belongs to class A?
Class A: C0: 1
Class B: C1: 2
K = 1/3 ---> # in class A over K = #
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Chapter 9
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For continous variable with 100 unique values, how many possible partitions are there along that variable in the recursive partitioning 99
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Suppose we are studying a 4-class classification problem with classification tree. What is the maximum value of entropy measure of impurity? 2 ---> log2(4) = 2
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How many possible partitions can we have for a categorical predictor with 4 categories? (Hint: you can enumerate them) 7 --> abcd, a-bcd, b-acd, c-dab, d-abc, ab-cd, ac-bd, ad-ac
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Is a full tree over fitted Yes
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Which tree is smaller, minimum error tree or best pruned tree ? Suppose they are different (sometimes they can be the same tree) Best Pruned Tree
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How do we measure the impurity of a partition in regression tree? Sum of Squared Deviations
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Is CART a model free algorithm Yes
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chapter 10
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For a logistic regression mode estimated as below, what is the probability of accepting personal loan offer for a person with income of 50K dollars (X= 50)? 1.22% i think. use the regression equation and sub in for x. =(1/(1+(EXP(6.3525-0.0392*(50))))) = probability
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Using the cutoff of 10%, should this person be classified as who will accept the offer? Yes
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What are the Odds of accepting the loan offer for this person? 0.0124 --> p/ (1-P) = the odds
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What is the odds ratio of income? 1.03998
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Is logistic regression a model- free algorithm NO
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