Homework week #2. Task 3.1(a). Use the ksvm or kknn function to find a good classifier using cross-validation. PDF

Title Homework week #2. Task 3.1(a). Use the ksvm or kknn function to find a good classifier using cross-validation.
Course Intro to Analytics Modeling
Institution Georgia Institute of Technology
Pages 2
File Size 59.7 KB
File Type PDF
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Summary

Using the same data set (credit_card_data.txt or credit_card_data-headers.txt) 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); (K-Fold CV)....


Description

HW2_3.1 a

HW2. Q3_1_a Part I: Load libraries, set the wd, set the seed, etc. rm(list = ls()) library(kknn) #install.packages("plotly") library(plotly) ## Loading required package: ggplot2 ## ## Attaching package: ’plotly’ ## The following object is masked from ’package:ggplot2’: ## ## last_plot ## The following object is masked from ’package:stats’: ## ## filter ## The following object is masked from ’package:graphics’: ## ## layout library(glue) set.seed(31) cc_data...


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