The Royalty Gold Corporation prospects for undiscovered gold deposits around the world. The company is currently investigating a possible site on the island of Milos off the coast of Greece in the Mediterranean. When prospecting, the company drills to collect soil and rock samples and then analyzes the chemical properties of the samples to help determine whether or not the site is likely to contain significant gold deposits. Gold-bearing ore is made up of various minerals including calaverite, sylvanite, and petzite. Sites with higher concentrations of these minerals are more likely to contain significant gold deposits. The file RoyalGold.xlsm represents the average levels of calaverite, sylvanite, and petzite in samples collected from previous various sites examined in previous prospecting expeditions. These data are grouped according to whether or not significant gold deposits were found at the location (1=significant, 2=insignificant).

a. Partition the data into a training set (with 60% of the observations) and validation set (with 40% of the observations)

b. Use the k-nearest neighbor technique to make a classifier for this data with normalized

inputs. What value of k seems to work best? How accurate is this procedure on the training and validation data sets?

c. Use a single classification tree to make a classifier for this data with at least 5 observations per terminal node, and the maximum tree depth to 5. make a graphic depiction of the tree using the validation data. How accurate is this procedure on the training and validation data sets?

d. Compare the kNN and Decision Tree and comment on the accuracy of these methods.