Description
1. (R and Python) Modify your program in Assignment #4 to do followings. For logistic regression, you can
use any optimization function in R or Python. However, you should not use the logistic regression function
directly.
a. Prompt the user whether to run regression or classification.
b. If regression is chosen, perform the linear regression as you did in Assignment #3. (You have nothing
to work on the regression algorithm in this assignment).
c. If classification is chosen, ask the user the filename of the training and test dataset. (Assume the
column location of the class variable is the same for both training and test dataset.)
d. If classification is chosen, prompt the user to choose (i) LDA and (ii) QDA, (iii) RDA, or (iv) Logistic
regression
e. Perform (i) LDA and (ii) QDA, (iii) RDA, or (iv) Logistic Regression depending on the choice by the
user. However, if the data has more than two classes, do not prompt (iv) Logistic Regression. Use a file
named “pid.dat” for the training and ‘pidtest.dat’ as the test data in this assignment.
f. The output file for classification generated by the program must look like below. (The numbers are
fictitious).
ID, Actual class, Resub pred, Pred Prob (if logistic regression is chosen)
—————————–
1, 1, 1, 0.878
2, 2, 2, 0.213
3, 1, 1, 0.675
(continue)
Confusion Matrix (Resubstitution)
———————————-
Predicted Class
1 2
Actual 1 239 14
Class 2 12 153
Model Summary (Resubstitution)
——————————
Overall accuracy = .793
Sensitivity = .894 #print this line only if there are two classes#
Specificity = .743 #print this line only if there are two classes#
ID, Actual class, Test pred, Pred Prob (if logistic regression is chosen)
—————————–
1, 1, 1, 0.878
2, 2, 2, 0.213
3, 1, 1, 0.675
(continue)
Confusion Matrix (Test)
———————————-
Predicted Class
1 2
Actual 1 239 14
Class 2 12 153
Model Summary (Test)
——————————
Overall accuracy = .793
Sensitivity = .894 #print this line only if there are two classes#
Specificity = .743 #print this line only if there are two classes#
