Appendix A10-Fold Cross Validation for Parameter SelectionCross Validation is the standard method for evaluation in empirical machine learning. It can also be usedfor parameter selection if we make sure to use the training set only.To select parameter A of algorithm A(X) over an enumerated range d E [A1,..., A] using dataset D, we dothe following:1. Split the data D into 10 disjoint folds.2. For each value of A e (A1,..., Ar]:(a) For i = 1 to 10Train A(A) on all folds but ith foldTest on ith fold and record the error on fold i(b) Compute the average performance of A on the 10 folds.3. Pick the value of A with the best average performanceNow, in the above, D only includes the training data and the parameter A is chosen without the knowledgeof the test data. We then re-train on the entire train set D using the chosen A and evaluate the result onthe test set.

Basically Cross-validation is a technique for evaluating a machine learning model and testing its…

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Computer Networking: A Top-Down Approach (7th Edition)

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Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Cross Validation is the standard method for evaluation in empirical machine learning. It can also be used for parameter selection if we make sure to use the training set only. To select parameter A of algorithm A(A) over an enumerated range d E [\1,., Ar] using dataset D, we do the following: