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Question at position 36 Which of the following strategies/tools should we choose in order to decide the k value in a kNN model?Nested holdout testingNested Cross-validationCumulative response curveCross-validationLearning CurveLift curveFitting GraphHoldout testing

Options
A.Nested holdout testing
B.Nested Cross-validation
C.Cumulative response curve
D.Cross-validation
E.Learning Curve
F.Lift curve
G.Fitting Graph
H.Holdout testing
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Step-by-Step Analysis
We need to evaluate each option in the context of selecting the k value for a kNN model and discuss why it would or would not be appropriate. Option: Nested holdout testing. This approach uses an outer holdout (test) set and an inner loop to tune hyperparameters, including k. It is a valid strategy for model selection because it provides an unbiased estimate of performance while tuning, so it can be used to decide an appropriate k. Option: Nested Cross-validation. Similar in spirit to nested holdout, but with cross-validation......Login to view full explanation

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