1. Use the k-NN method for k=1,3,5, and 7 to classify each of the 15 points in the test set by using the training set.
   1. Make a table that shows the classification for each point in the test set and for each k.
   2. Highlight the errors in red. Compute the error rate for each k.
   3. Based on your answers in part b), what value of k should you use to classify new data?

1. Use the CART method to classify the flowers. Perform two splits. If there are equally good splits available, you may choose which to use.
   1. What variable was chosen for the first split and where was it split?
   2. What was the GINI for each group after the first split?
   3. What variables were chosen for the second split and where were they split?
   4. What was the GINI for each group after the second split?
   5. What was the total GINI after two splits?
   6. Draw the CART.
   7. Apply the model to the test set. What was the error rate on the test set?

1. Use logistic regression to classify the flowers. Using Iris-setosa as the base level, set up the spreadsheet to calculate the probabilities using the model based on the following equations:

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