Homework 2 with Correct Answers Georgia Institute Of Technology ISYE 6501

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); Cross Validation is a model evaluation method to see how well our model has performed and prevent over fitting. Supposed we receive a dataset with n data points, we do not use all these data points for training instead we split them into training data and testing data . We use different iterations of this training data and test data to check the efficiency of our model . Different type of cross validation methods that I came across are – Holdout method, Leave one out cross validation and K-fold cross validation . For k-nearest-neighbors model (knn) model , I am going to use the Leave one out cross validation . In this case – if I have n datapoint, I leave one out and use n-1 for training purpose . Display my_model to find error and Best K . (In this case best k = 58) Now, let us check the accuracy for our model : (b) splitting the data into training, validation, and test data sets (pick either KNN or SVM; the other is optional). Now our next step is to split our data into – training , validation, test data – Here we have decided to take the approach to split 80% of the data as training and