Perform LDA on the training data in order to predict mpg01 using the variables that seemed most associated with mpg01 in (b). What is the test error of the model obtained?

1) Perform LDA on the training data in order to predict mpg01 using the variables that seemed most associated with mpg01 in (b). What is the test error of the model obtained?

2) Perform KNN on the training data, with several values of K, in order to predict mpg01. Use only the variables that seemed most associated with mpg01 in (b). What test errors do you obtain? Which value of K seems to perform the best on this data set?

In the summary document, include your answers to the questions with reasoning/explanation; if R is used to produce outputs, you need to include both the R script (copy & paste at the end of the document) and the critical output (as part of the answers or explanation) to show your work.

The K-NN (K-Nearest Neighbors) algorithm is based on comparing an unknown observation with its nearest known neighbors. In other words, the class or label of the unknown (unlabeled) observation is determined based on the class of its k neighbors (we define k) in the n-dimensional space (n is the number of attributes in the training dataset).

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Requirements: to be answered

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