HOMEWORK 2 – SAMPLE SOLUTIONS IMPORTANT NOTE, Georgia Tech, Graded A+

HOMEWORK 2 – SAMPLE SOLUTIONS IMPORTANT NOTE, Georgia Tech, Graded A+ Document Content and Description Below HOMEWORK 2 – SAMPLE SOLUTIONS IMPORTANT NOTE These homework solutions show multiple approaches and some optional extensions for most of the questions in the assignment. You don’t need to submit all this in your assignments; they’re included here just to help you learn more – because remember, the main goal of the homework assignments, and of the entire course, is to help you learn as much as you can, and develop your analytics skills as much as possible! Question 3.1 Using the same data set (credit_card_ or credit_card_) as in Question 2.2, use the ksvm or kknn function to find a good classifier: (a) using cross-validation (do this for the k-nearest-neighbors model; SVM is optional); and (b) splitting the data into training, validation, and test data sets (pick either KNN or SVM; the other is optional). SOLUTIONS: (a) There are different ways to do this. Three different methods are shown in solution 3.1-a.R. Just having one method is fine for your homework solutions. All three are shown below, for learning purposes. Another optional component shown below is using cross-validation for ksvm; this too did not need to be included in your solutions. METHOD 1 The simplest approach, using kknn’s built-in cross-validation, is fine as a solution. uses leave-one-out cross-validation, which sounds like a different type of cross-validation that I didn’t mention in the videos – but if you watched the videos, you know it implicitly already! For each data point, it fits a model to all the other data points, and uses the remaining data point as a test – in other This study source was downloaded by from CourseH on :58:23 GMT -05:00 Last updated: 7 months ago