SVM and KNN model libraries in R were given as examples. You can use Python or Matlab libraries. Ch9-svm-lab.ipynb Download Ch9-svm-lab.ipynb


Using the support vector machine function ksvm contained in the R package kernlab, or the library from our course labs. Find a good classifier for this data. Show the equation of your classifier, and how well it classifies the data points in the full data set. Please have training, validation, and testing division. You can try other kernels, too. Report accuracy of you model.
Using the k-nearest-neighbors classification function kknn contained in the R kknn package, suggest a good value of k. Don’t forget to scale the data (scale=TRUE in kknn). Again you can use the library/package from books labs (Resources - Second Edition — An Introduction to Statistical Learning (statlearning.com) Links to an external site.).
Fit a decision tree model again using tree package. (or using Matlab/Python)
Fit a random forest model using randomForest package in R. (or using Matlab/Python)
Plot accuracy of training, validation, and testing as a boxplot and compare the models.