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ISYE 6501 - Homework 2 Jacob Wilson September 6, 2018 Question 3.1(a) – KKNN with Cross Validation Answer: In this problem, I utilized the “caret” library and the train function with a “kknn” method to perform 10 -fold cross validation. The following is the results: The most accurate classification was at k=23 because the accuracy is the highest and the data is classified against the most neighbors, reducing the risk. After retraining the model with k=23, accuracy was 84.3%. RStudio Script: install.packages("kknn", repos="http://cran.rstudio.com/") ## Installing package into 'C:/Users/jacob/Documents/R/win-library/3.5' ## (as 'lib' is unspecified) ## package 'kknn' successfully unpacked and MD5 sums checked ## ## The downloaded binary packages are in ## C:\Users\jacob\AppData\Local\Temp\Rtmp29XlTm\downloaded_packages install.packages("caret", repos ="http://cran.rstudio.com/") ## Installing package into 'C:/Users/jacob/Documents/R/win-library/3.5' ## (as 'lib' is unspecified) ## package 'caret' successfully unpacked and MD5 sums checked ## kmax Accuracy Kappa 5 0.8304588 0.6538483 7 0.8344980 0.6633484 9 0.8344980 0.6634414 11 0.8354980 0.6653672 13 0.8354980 0.6653672 15 0.8354980 0.6653672 17 0.8360108 0.6663338 19 0.8360108 0.6663338 21 0.8360108 0.6663338 23 0.8360108 0.6663338 ## The downloaded binary packages are in ## C:\Users\jacob\AppData\Local\Temp\Rtmp29XlTm\downloaded_packages install.packages("e1071", repos= "http://cran.rstudio.com/") ## Installing package into 'C:/Users/jacob/Documents/R/win-library/3.5' ## (as 'lib' is unspecified) ## package 'e1071' successfully unpacked and MD5 sums checked ## ## The downloaded binary packages are in ## C:\Users\jacob\AppData\Local\Temp\Rtmp29XlTm\downloaded_packages library(caret) ## Loading required package: lattice ## Loading required package: ggplot2 library(e1071) library(kknn) ## ## Attaching package: 'kknn' ## The following object is masked from 'package:caret': ## ## contr.dummy Importing the data from “credit_card_data-headers”, establishing the working directory, and ensuring the results are repeatable… setwd("C:/Users/jacob/Desktop/ISYE 6501/Homework 1 - 30 AUG 2018") cc_data <- read.table("credit_card_data-headers.txt", header = TRUE) set.seed(313) dp <- createDataPartition(cc_data$R1, p = 0.6, list = FALSE) train <- cc_data[dp,] trainX <- cc_data[dp, 1:10] trainY <- as.factor(cc_data[dp, 11]) test <- cc_data[-dp,] testX <- cc_data[-dp, 1:10] testY <- as.factor(cc_data[-dp, 11]) The control sets the parameters for the k-fold cross validation. This will be 10-fold cross validation that is repeated 10 times. control <- trainControl(method = "repeatedcv", number = 10, repeats = 5) Training the model with the Caret Package. Note: Explaining the inputs - train(Classes, Data, Scaling data from 0 to 1, tuneLength = K over 10 values and uses the best to train the final model, trControl = Implements the control establish above) model_knn <- train(trainX, trainY, method = "kknn", preProcess = c("range"), tuneLength = 10, t rControl = control) print(model_knn) ## k-Nearest Neighbors ## ## 393 samples ## 10 predictor ## 2 classes: '0', '1' ## ## Pre-processing: re-scaling to [0

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