rm(list=ls())
library(ISLR)
library(tree)
attach(Carseats)
head(Carseats)
## Sales CompPrice Income Advertising Population Price ShelveLoc Age
## 1 9.50 138 73 11 276 120 Bad 42
## 2 11.22 111 48 16 260 83 Good 65
## 3 10.06 113 35 10 269 80 Medium 59
## 4 7.40 117 100 4 466 97 Medium 55
## 5 4.15 141 64 3 340 128 Bad 38
## 6 10.81 124 113 13 501 72 Bad 78
## Education Urban US
## 1 17 Yes Yes
## 2 10 Yes Yes
## 3 12 Yes Yes
## 4 14 Yes Yes
## 5 13 Yes No
## 6 16 No Yes
range(Sales)
## [1] 0.00 16.27
#creat a categorical varibale based on sales
High=ifelse(Sales>=8,"Yes","No")
Carseats=data.frame(Carseats, High)
head(Carseats)
## Sales CompPrice Income Advertising Population Price ShelveLoc Age
## 1 9.50 138 73 11 276 120 Bad 42
## 2 11.22 111 48 16 260 83 Good 65
## 3 10.06 113 35 10 269 80 Medium 59
## 4 7.40 117 100 4 466 97 Medium 55
## 5 4.15 141 64 3 340 128 Bad 38
## 6 10.81 124 113 13 501 72 Bad 78
## Education Urban US High
## 1 17 Yes Yes Yes
## 2 10 Yes Yes Yes
## 3 12 Yes Yes Yes
## 4 14 Yes Yes No
## 5 13 Yes No No
## 6 16 No Yes Yes
#remove first varilabe
Carseats=Carseats[,-1]
names(Carseats)
## [1] "CompPrice" "Income" "Advertising" "Population" "Price"
## [6] "ShelveLoc" "Age" "Education" "Urban" "US"
## [11] "High"
#split data into training ans test set
set.seed(2)
train=sample(1:nrow(Carseats),nrow(Carseats)/2)
test=-train
test
## [1] -74 -281 -229 -67 -374 -373 -51 -328 -184 -216 -391 -93 -296 -70
## [15] -157 -329 -375 -87 -170 -29 -252 -147 -317 -57 -131 -392 -56 -134
## [29] -359 -50 -4 -61 -299 -319 -189 -398 -308 -104 -242 -55 -354 -107
## [43] -42 -59 -337 -283 -346 -124 -177 -285 -3 -6 -238 -323 -96 -399
## [57] -271 -340 -210 -243 -262 -301 -212 -88 -289 -379 -130 -154 -73 -22
## [71] -91 -103 -14 -369 -60 -246 -94 -345 -334 -384 -113 -215 -8 -128
## [85] -64 -270 -306 -102 -365 -106 -303 -123 -118 -173 -142 -327 -322 -381
## [99] -35 -133 -305 -378 -293 -247 -85 -176 -265 -339 -44 -38 -318 -213
## [113] -108 -165 -237 -232 -248 -32 -269 -160 -11 -69 -273 -325 -397 -209
## [127] -155 -84 -366 -92 -396 -284 -7 -258 -388 -352 -141 -390 -49 -105
## [141] -47 -400 -163 -78 -114 -187 -382 -79 -121 -259 -46 -90 -225 -98
## [155] -192 -387 -275 -358 -20 -71 -202 -226 -280 -180 -241 -153 -362 -228
## [169] -370 -125 -223 -24 -357 -204 -356 -179 -253 -31 -145 -380 -208 -126
## [183] -220 -254 -26 -286 -19 -347 -393 -17 -23 -266 -203 -302 -363 -341
## [197] -41 -37 -315 -27
training_data=Carseats[train,]
testing_data=Carseats[test, ]
testing_High=High[test]
#fit thr tree model using training data
tree_model=tree(High~.,training_data)
plot(tree_model)
text(tree_model, pretty = 0)
#check how the model is doing using the test data
tree_pred=predict(tree_model, testing_data, type="class")
mean(tree_pred!=testing_High)
## [1] 0.285
#PRUNE the tree
##cross validation to check whre to stop pruning
set.seed(3)
cv_tree=cv.tree(tree_model, FUN=prune.misclass)
names(cv_tree)
## [1] "size" "dev" "k" "method"
plot(cv_tree$size, cv_tree$dev, type="b")
##prune the tree
pruned_model=prune.misclass(tree_model, best=9)
plot(pruned_model)
text(pruned_model, pretty=0)
##check how it is doing
tree_pred=predict(pruned_model, testing_data, type="class")
mean(tree_pred !=testing_High)
## [1] 0.23