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# Untitled

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1. ### question 4 hint ####
2. library(MASS)
3. library(class)
4. Boston\$medv01 <- ifelse(Boston\$medv > median(Boston\$medv),1,0)
5. names(Boston)
6.
7. # Choice of subsets
8. plot(Boston)
9.
10. #subset 1: "lstat","rm"
11. #subset 2: "lstat", "rm","age","rad"
12. #subset 3: all predictors
13.
14. Boston\$medv <- NULL
15. names(Boston)
16.
17. ### try three values of K #######
18. ##### K=1 #######
19. x.train <- Boston[,c("lstat","rm")]
20. y.train <- Boston[,"medv01"]
21. test.err <- mean(knn.cv(x.train, y.train, k=1) !=y.train)
22. test.err
23.
24. x.train <- Boston[,c("lstat", "rm","age","rad")]
25. y.train <- Boston[,14]
26. test.err <- mean(knn.cv(x.train, y.train, k=1) !=y.train)
27. test.err
28.
29. x.train <- Boston[,-14]### using all the predictors
30. y.train <- Boston[,14]
31. test.err <- mean(knn.cv(x.train, y.train, k=1) !=y.train)
32. test.err
33.
34. x.train <- Boston[,c("lstat","rm")]
35. y.train <- Boston[,"medv01"]
36. test.err <- mean(knn.cv(x.train, y.train, k=3) !=y.train)
37. test.err
38.
39. x.train <- Boston[,c("lstat", "rm","age","rad")]
40. y.train <- Boston[,14]
41. test.err <- mean(knn.cv(x.train, y.train, k=3) !=y.train)
42. test.err
43.
44. x.train <- Boston[,-14]### using all the predictors
45. y.train <- Boston[,14]
46. test.err <- mean(knn.cv(x.train, y.train, k=3) !=y.train)
47. test.err
48.
49. x.train <- Boston[,c("lstat","rm")]
50. y.train <- Boston[,"medv01"]
51. test.err <- mean(knn.cv(x.train, y.train, k=10) !=y.train)
52. test.err
53.
54. x.train <- Boston[,c("lstat", "rm","age","rad")]
55. y.train <- Boston[,14]
56. test.err <- mean(knn.cv(x.train, y.train, k=10) !=y.train)
57. test.err
58.
59. x.train <- Boston[,-14]### using all the predictors
60. y.train <- Boston[,14]
61. test.err <- mean(knn.cv(x.train, y.train, k=10) !=y.train)
62. test.err
63.
64. ###SCALED DATA####
65. X.train <- Boston[,c("lstat","rm")]
66. X.train.scaled <- scale(X.train)
67. y.train <- Boston[,"medv01"]
68. test.err <- mean(knn.cv(X.train.scaled, y.train, k=1) !=y.train)
69. test.err
70.
71. X.train <- Boston[,c("lstat", "rm","age","rad")]
72. X.train.scaled <- scale(X.train)
73. y.train.scaled <- Boston[,14]
74. test.err <- mean(knn.cv(X.train.scaled, y.train, k=1) !=y.train.scaled)
75. test.err
76.
77. X.train <- Boston[,-14]### using all the predictors
78. X.train.scaled <- scale(X.train)
79. y.train.scaled <- Boston[,14]
80. test.err <- mean(knn.cv(X.train.scaled, y.train, k=1) !=y.train)
81. test.err
82.
83. X.train <- Boston[,c("lstat","rm")]
84. X.train.scaled <- scale(X.train)
85. y.train <- Boston[,"medv01"]
86. test.err <- mean(knn.cv(X.train.scaled, y.train, k=3) !=y.train)
87. test.err
88.
89. X.train <- Boston[,c("lstat", "rm","age","rad")]
90. X.train.scaled <- scale(X.train)
91. y.train.scaled <- Boston[,14]
92. test.err <- mean(knn.cv(X.train.scaled, y.train, k=3) !=y.train.scaled)
93. test.err
94.
95. X.train <- Boston[,-14]### using all the predictors
96. X.train.scaled <- scale(X.train)
97. y.train.scaled <- Boston[,14]
98. test.err <- mean(knn.cv(X.train.scaled, y.train, k=3) !=y.train)
99. test.err
100.
101. X.train <- Boston[,c("lstat","rm")]
102. X.train.scaled <- scale(X.train)
103. y.train <- Boston[,"medv01"]
104. test.err <- mean(knn.cv(X.train.scaled, y.train, k=10) !=y.train)
105. test.err
106.
107. X.train <- Boston[,c("lstat", "rm","age","rad")]
108. X.train.scaled <- scale(X.train)
109. y.train.scaled <- Boston[,14]
110. test.err <- mean(knn.cv(X.train.scaled, y.train, k=10) !=y.train.scaled)
111. test.err
112.
113. X.train <- Boston[,-14]### using all the predictors
114. X.train.scaled <- scale(X.train)
115. y.train.scaled <- Boston[,14]
116. test.err <- mean(knn.cv(X.train.scaled, y.train, k=10) !=y.train)
117. test.err
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