We also used the auto dataset two weeks ago in lab 6. We used it with LDA and QDA. Both methods in R provide a CV argument that will compute a LOOCV estimate for us. If we want to compute a k-fold cross validation estimate when k is not equal to the number of instances, we have to either write our own code or find another library to use. Here we will write our own code! Write a function that accepts a dataframe, a model-building function (either lda or qda), and a value for K and returns an error estimate and its variance for k-fold cross validation. Use this function to generate a table. Compare these values to using the training set and a validation set to estimate the error rates, too. Finally, include a paragraph summarizing and explaining the results.

Note: Attached is the lab6 where you can find the section 2 auto dataset classification metric logistic regression, linear discriminant analysis (lda) and quadratic discriminant analysis (qda) for this homework