Another Book

An Introduction to Statistical Learning with Applications in R



Data From ISLR


  • Advertising.csv
  • Income1.csv
  • Income2.csv
  • knnexample.csv
  • line.csv
  • Premier League 2011-12 (OPTA).xlsx



  • Advertising.csv
  • Auto.data
  • Auto.csv
  • College.csv
  • Ch10Ex11.csv
  • Credit.csv
  • Income1.csv (Figure 2.2)
  • Income2.csv (Figure 2.3)
  • Heart.csv
  • Smarket.csv
  • Caravan.csv



    R Code

  • ch5.R


    Homeworks


    • Homework 1. Ch 2: 4,6,10
    • Homework 2. Ch 3: 1,2,4,6
    • Homework 3. Ch 3:8,9,10,11abf, 14
    • Homework 4.
      • For Three.csv
        • Make an LDA Model (label model 1).
        • Find Accuracy, Specificty and Sensitivity.
        • Make a QDA model and compare Accuracy, Specificty and Sensitivity.
      • For Quad.csv
        • Make a QDA model (label model 2).
        • Find Accuracy, Specificty and Sensitivity.
        • Make a Logistic Regression and compare Accuracy, Specificty and Sensitivity.
      • For KNNData.csv
        • Make a KNN Model for various k.
        • Evaluate the models and select the k with the highest accuracy. Label the model with the highest accuracy model 3.
        • Find Specificty and Sensitivity.
        • Make an LDA and compare accuracy.
      • Extra Credit. For each of the three models above Model 1, Model 2, and Model 3 make a 2D graph displaying
        • the datapoints color coded for a, b or a, b and c and
        • the decision boundary.



      Videos


    • April 6th Class
    • April 13th Class
    • April 20th Class
    • /media/frank/FrankWork/home/courses/21spring/MA6520/Videos