Georgia Tech ISYE 6501 – Intro Analytics Modeling Due Date: 1/22/20

Georgia Tech ISYE 6501 – Intro Analytics Modeling Due Date: 1/22/20 Homework 2. 100% Proven pass rate. Document Content and Description Below ISYE 6501 – Intro Analytics Modeling Due Date: 1/22/20 Homework 2 Question 3.1 Using the same data set...as in Question 2.2, use the ksvm or kknn function to find a good classifier: a) using c ross-validation (do this for the k-nearest-neighbors model; SVM is optional); and Code for 3.1a is in the file named "3_1a_final.R". For the k-nearest-neighbors model, the highest accuracy was achieved using 5 as the 'k' value. The plot below shows cross-validated model accuracties for k values from 1 to 30. I split the data 80% for cross-validation and 20% for testing. During cross-validation, the model with k=5 achieved an accuracy of 85.5%. Testing the model of k=5 on the remaining 20% test data set resulted in an accuracy of 83.1%. b) splitting the data into training, validation, and test data sets (pick either KNN or SVM; the other is optional). Code for 3.1b is in the file named "3_1b_final.R" I chose to split the data 60% train, 20% validate, and 20% test. In the validation stage, I tested k values from 1 to 30. Unlike the cross validation exercise completed in part a, maximum accuracy was not achieved until 12 (84.7%). I'm not sure why k=5 resulted in a much lower 79.4% when using train/validate/test. There are always both real and random effects. It's possible that some This study source was downloaded by from CourseH on :30:22 GMT -05:00