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Apply for the naive Bayes klarR program with cross-validation to the classification of your stock range

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

6.1. C(HighRisk or LowRisk), using lagged ranges as x-variables.  How well does NB do compared to knn using the kcvSearch to select k from the train data?  To make this a fair comparison, you need to run knn on the same train-test split that you gave to the NB program.

6.2  Add 8 more lags to your stock range data - the Hi-risk vs Lo-risk data.  Do that in Excel using our best practices method.  Try running kNN and klaR with that data and with the same train-test split. 

       (a) What changes do you observe in the performance? 

       (b) Did adding more regressors (more lags) effect the choice of k in kNN? 

       (c) Did that slow down the kcvSearch program?  Why would that matter?

 

 

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