과제7_채명지
chaemj
2024-06-04
OJ: Orange Juice Data
변수 설명
head(OJ)## Purchase WeekofPurchase StoreID PriceCH PriceMM DiscCH DiscMM SpecialCH
## 1 CH 237 1 1.75 1.99 0.00 0.0 0
## 2 CH 239 1 1.75 1.99 0.00 0.3 0
## 3 CH 245 1 1.86 2.09 0.17 0.0 0
## 4 MM 227 1 1.69 1.69 0.00 0.0 0
## 5 CH 228 7 1.69 1.69 0.00 0.0 0
## 6 CH 230 7 1.69 1.99 0.00 0.0 0
## SpecialMM LoyalCH SalePriceMM SalePriceCH PriceDiff Store7 PctDiscMM
## 1 0 0.500000 1.99 1.75 0.24 No 0.000000
## 2 1 0.600000 1.69 1.75 -0.06 No 0.150754
## 3 0 0.680000 2.09 1.69 0.40 No 0.000000
## 4 0 0.400000 1.69 1.69 0.00 No 0.000000
## 5 0 0.956535 1.69 1.69 0.00 Yes 0.000000
## 6 1 0.965228 1.99 1.69 0.30 Yes 0.000000
## PctDiscCH ListPriceDiff STORE
## 1 0.000000 0.24 1
## 2 0.000000 0.24 1
## 3 0.091398 0.23 1
## 4 0.000000 0.00 1
## 5 0.000000 0.00 0
## 6 0.000000 0.30 0str(OJ)## 'data.frame': 1070 obs. of 18 variables:
## $ Purchase : Factor w/ 2 levels "CH","MM": 1 1 1 2 1 1 1 1 1 1 ...
## $ WeekofPurchase: num 237 239 245 227 228 230 232 234 235 238 ...
## $ StoreID : num 1 1 1 1 7 7 7 7 7 7 ...
## $ PriceCH : num 1.75 1.75 1.86 1.69 1.69 1.69 1.69 1.75 1.75 1.75 ...
## $ PriceMM : num 1.99 1.99 2.09 1.69 1.69 1.99 1.99 1.99 1.99 1.99 ...
## $ DiscCH : num 0 0 0.17 0 0 0 0 0 0 0 ...
## $ DiscMM : num 0 0.3 0 0 0 0 0.4 0.4 0.4 0.4 ...
## $ SpecialCH : num 0 0 0 0 0 0 1 1 0 0 ...
## $ SpecialMM : num 0 1 0 0 0 1 1 0 0 0 ...
## $ LoyalCH : num 0.5 0.6 0.68 0.4 0.957 ...
## $ SalePriceMM : num 1.99 1.69 2.09 1.69 1.69 1.99 1.59 1.59 1.59 1.59 ...
## $ SalePriceCH : num 1.75 1.75 1.69 1.69 1.69 1.69 1.69 1.75 1.75 1.75 ...
## $ PriceDiff : num 0.24 -0.06 0.4 0 0 0.3 -0.1 -0.16 -0.16 -0.16 ...
## $ Store7 : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 2 2 2 2 2 ...
## $ PctDiscMM : num 0 0.151 0 0 0 ...
## $ PctDiscCH : num 0 0 0.0914 0 0 ...
## $ ListPriceDiff : num 0.24 0.24 0.23 0 0 0.3 0.3 0.24 0.24 0.24 ...
## $ STORE : num 1 1 1 1 0 0 0 0 0 0 ...- 시트러스 힐 또는 미닛메이드 오렌지 주스 구입과 관련된 데이터
- 1070개 관측치의 18개의 변수들로 이루어짐.
- 변수: 5개의 범주형 변수와 13개의 수치형 변수가 있음.
Purchase: 소비자가 Citrus Hill 또는 Minute Maid Orange Juice를
구매했는지를 나타내는 요인 (CH 또는 MM). WeekofPurchase: 구매가 이루어진
주.
StoreID: 구매가 이루어진 매장의 식별자.
PriceCH: 제품 CH의 정가.
PriceMM: 제품 MM의 정가.
DiscCH: 제품 CH에 제공된 할인.
DiscMM: 제품 MM에 제공된 할인.
SpecialCH: 제품 CH에 대한 특별 프로모션 여부 표시
SpecialMM: 제품 MM에 대한 특별 프로모션 여부 표시 LoyalCH: 제품 CH에
대한 고객 브랜드 충성도 측정값.
SalePriceMM: 제품 MM의 판매 가격.
SalePriceCH: 제품 CH의 판매 가격.
PriceDiff: 제품 MM과 CH의 판매 가격 차이 (SalePriceMM -
SalePriceCH).
Store7: 판매가 매장 7에서 이루어졌는지 여부를 나타내는 요인 (“No”와
“Yes” 수준을 가짐).
PctDiscMM: 제품 MM의 할인율.
PctDiscCH: 제품 CH의 할인율.
ListPriceDiff: 제품 MM과 CH의 정가 차이 (PriceMM - PriceCH).
STORE: 판매가 이루어진 5개의 가능한 매장
summary(OJ)## Purchase WeekofPurchase StoreID PriceCH PriceMM
## CH:653 Min. :227.0 Min. :1.00 Min. :1.690 Min. :1.690
## MM:417 1st Qu.:240.0 1st Qu.:2.00 1st Qu.:1.790 1st Qu.:1.990
## Median :257.0 Median :3.00 Median :1.860 Median :2.090
## Mean :254.4 Mean :3.96 Mean :1.867 Mean :2.085
## 3rd Qu.:268.0 3rd Qu.:7.00 3rd Qu.:1.990 3rd Qu.:2.180
## Max. :278.0 Max. :7.00 Max. :2.090 Max. :2.290
## DiscCH DiscMM SpecialCH SpecialMM
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.05186 Mean :0.1234 Mean :0.1477 Mean :0.1617
## 3rd Qu.:0.00000 3rd Qu.:0.2300 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :0.50000 Max. :0.8000 Max. :1.0000 Max. :1.0000
## LoyalCH SalePriceMM SalePriceCH PriceDiff Store7
## Min. :0.000011 Min. :1.190 Min. :1.390 Min. :-0.6700 No :714
## 1st Qu.:0.325257 1st Qu.:1.690 1st Qu.:1.750 1st Qu.: 0.0000 Yes:356
## Median :0.600000 Median :2.090 Median :1.860 Median : 0.2300
## Mean :0.565782 Mean :1.962 Mean :1.816 Mean : 0.1465
## 3rd Qu.:0.850873 3rd Qu.:2.130 3rd Qu.:1.890 3rd Qu.: 0.3200
## Max. :0.999947 Max. :2.290 Max. :2.090 Max. : 0.6400
## PctDiscMM PctDiscCH ListPriceDiff STORE
## Min. :0.0000 Min. :0.00000 Min. :0.000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.140 1st Qu.:0.000
## Median :0.0000 Median :0.00000 Median :0.240 Median :2.000
## Mean :0.0593 Mean :0.02731 Mean :0.218 Mean :1.631
## 3rd Qu.:0.1127 3rd Qu.:0.00000 3rd Qu.:0.300 3rd Qu.:3.000
## Max. :0.4020 Max. :0.25269 Max. :0.440 Max. :4.000(a) Create a training set containing a random sample of 800 observations, and a test set containing the remaining observations.
set.seed(1)
train.oj<-sample(dim(OJ)[1],800)
oj_train<-OJ[train.oj, ]
oj_test<-OJ[-train.oj, ](b) Fit a tree to the training data, with Purchase as the response and the other variables as predictors. Use the summary() function to produce summary statistics about the tree, and describe the results obtained. What is the training error rate? How many terminal nodes does the tree have?
tree_oj<-tree(Purchase~., data=oj_train)
summary(tree_oj)##
## Classification tree:
## tree(formula = Purchase ~ ., data = oj_train)
## Variables actually used in tree construction:
## [1] "LoyalCH" "PriceDiff" "SpecialCH" "ListPriceDiff"
## [5] "PctDiscMM"
## Number of terminal nodes: 9
## Residual mean deviance: 0.7432 = 587.8 / 791
## Misclassification error rate: 0.1588 = 127 / 800(c) Type in the name of the tree object in order to get a detailed text output. Pick one of the terminal nodes, and interpret the information displayed.
tree_oj## node), split, n, deviance, yval, (yprob)
## * denotes terminal node
##
## 1) root 800 1073.00 CH ( 0.60625 0.39375 )
## 2) LoyalCH < 0.5036 365 441.60 MM ( 0.29315 0.70685 )
## 4) LoyalCH < 0.280875 177 140.50 MM ( 0.13559 0.86441 )
## 8) LoyalCH < 0.0356415 59 10.14 MM ( 0.01695 0.98305 ) *
## 9) LoyalCH > 0.0356415 118 116.40 MM ( 0.19492 0.80508 ) *
## 5) LoyalCH > 0.280875 188 258.00 MM ( 0.44149 0.55851 )
## 10) PriceDiff < 0.05 79 84.79 MM ( 0.22785 0.77215 )
## 20) SpecialCH < 0.5 64 51.98 MM ( 0.14062 0.85938 ) *
## 21) SpecialCH > 0.5 15 20.19 CH ( 0.60000 0.40000 ) *
## 11) PriceDiff > 0.05 109 147.00 CH ( 0.59633 0.40367 ) *
## 3) LoyalCH > 0.5036 435 337.90 CH ( 0.86897 0.13103 )
## 6) LoyalCH < 0.764572 174 201.00 CH ( 0.73563 0.26437 )
## 12) ListPriceDiff < 0.235 72 99.81 MM ( 0.50000 0.50000 )
## 24) PctDiscMM < 0.196196 55 73.14 CH ( 0.61818 0.38182 ) *
## 25) PctDiscMM > 0.196196 17 12.32 MM ( 0.11765 0.88235 ) *
## 13) ListPriceDiff > 0.235 102 65.43 CH ( 0.90196 0.09804 ) *
## 7) LoyalCH > 0.764572 261 91.20 CH ( 0.95785 0.04215 ) *summary(tree_oj)##
## Classification tree:
## tree(formula = Purchase ~ ., data = oj_train)
## Variables actually used in tree construction:
## [1] "LoyalCH" "PriceDiff" "SpecialCH" "ListPriceDiff"
## [5] "PctDiscMM"
## Number of terminal nodes: 9
## Residual mean deviance: 0.7432 = 587.8 / 791
## Misclassification error rate: 0.1588 = 127 / 800(d) Create a plot of the tree, and interpret the results.
plot(tree_oj)
text(tree_oj, pretty=0)
LoyalCH가 가장 중요한 변수로 보임. 가장 많이 반복되었고 처음과 두번째
level을 분류할 때 사용됨.
(e) Predict the response on the test data, and produce a confusion matrix comparing the test labels to the predicted test labels. What is the test error rate?
pred_oj<-predict(tree_oj,oj_test,type="class")
table(oj_test$Purchase,pred_oj)## pred_oj
## CH MM
## CH 160 8
## MM 38 64#test error rate
1-(160+64)/(160+8+38+64)## [1] 0.1703704test error rate은 17%이다.
(f) Apply the cv.tree() function to the training set in order to determine the optimal tree size.
cvtr_oj<-cv.tree(tree_oj,FUN=prune.misclass)
cvtr_oj## $size
## [1] 9 8 7 4 2 1
##
## $dev
## [1] 150 150 149 158 172 315
##
## $k
## [1] -Inf 0.000000 3.000000 4.333333 10.500000 151.000000
##
## $method
## [1] "misclass"
##
## attr(,"class")
## [1] "prune" "tree.sequence"(g) Produce a plot with tree size on the x-axis and cross-validated classification error rate on the y-axis.
plot(cvtr_oj$size, cvtr_oj$dev,type='b', xlab="Tree Size", ylab="Deviance")
(h) Which tree size corresponds to the lowest cross-validated
classification error rate?
tree 크기가 7일 때 error rate이 가장 낮다.
(i) Produce a pruned tree corresponding to the optimal tree size obtained using cross-validation. If cross-validation does not lead to selection of a pruned tree, then create a pruned tree with five terminal nodes.
prtr_oj <- prune.tree(tree_oj, best = 7)
plot(prtr_oj)
text(prtr_oj, pretty = 0)
(j) Compare the training error rates between the pruned and unpruned trees. Which is higher?
summary(tree_oj)##
## Classification tree:
## tree(formula = Purchase ~ ., data = oj_train)
## Variables actually used in tree construction:
## [1] "LoyalCH" "PriceDiff" "SpecialCH" "ListPriceDiff"
## [5] "PctDiscMM"
## Number of terminal nodes: 9
## Residual mean deviance: 0.7432 = 587.8 / 791
## Misclassification error rate: 0.1588 = 127 / 800summary(prtr_oj)##
## Classification tree:
## snip.tree(tree = tree_oj, nodes = c(10L, 4L))
## Variables actually used in tree construction:
## [1] "LoyalCH" "PriceDiff" "ListPriceDiff" "PctDiscMM"
## Number of terminal nodes: 7
## Residual mean deviance: 0.7748 = 614.4 / 793
## Misclassification error rate: 0.1625 = 130 / 800misclassification error rate이 unpruned일때는 0.1588, pruned일 때는 0.1625로 pruned일 때 더 높다.
(k) Compare the test error rates between the pruned and unpruned trees. Which is higher?
pred_ojpr<-predict(prtr_oj,oj_test,type="class")
table(oj_test$Purchase,pred_ojpr)## pred_ojpr
## CH MM
## CH 160 8
## MM 36 66#test error rate
1-(160+66)/(160+8+36+66)## [1] 0.162963test error rate은 unpruned일 때17%, pruned일 때 약 16.3%로 pruned일 때 더 낮다.
Caravan: The Insurance Company(TIC) Benchmark
변수 설명
head(Caravan)## MOSTYPE MAANTHUI MGEMOMV MGEMLEEF MOSHOOFD MGODRK MGODPR MGODOV MGODGE MRELGE
## 1 33 1 3 2 8 0 5 1 3 7
## 2 37 1 2 2 8 1 4 1 4 6
## 3 37 1 2 2 8 0 4 2 4 3
## 4 9 1 3 3 3 2 3 2 4 5
## 5 40 1 4 2 10 1 4 1 4 7
## 6 23 1 2 1 5 0 5 0 5 0
## MRELSA MRELOV MFALLEEN MFGEKIND MFWEKIND MOPLHOOG MOPLMIDD MOPLLAAG MBERHOOG
## 1 0 2 1 2 6 1 2 7 1
## 2 2 2 0 4 5 0 5 4 0
## 3 2 4 4 4 2 0 5 4 0
## 4 2 2 2 3 4 3 4 2 4
## 5 1 2 2 4 4 5 4 0 0
## 6 6 3 3 5 2 0 5 4 2
## MBERZELF MBERBOER MBERMIDD MBERARBG MBERARBO MSKA MSKB1 MSKB2 MSKC MSKD
## 1 0 1 2 5 2 1 1 2 6 1
## 2 0 0 5 0 4 0 2 3 5 0
## 3 0 0 7 0 2 0 5 0 4 0
## 4 0 0 3 1 2 3 2 1 4 0
## 5 5 4 0 0 0 9 0 0 0 0
## 6 0 0 4 2 2 2 2 2 4 2
## MHHUUR MHKOOP MAUT1 MAUT2 MAUT0 MZFONDS MZPART MINKM30 MINK3045 MINK4575
## 1 1 8 8 0 1 8 1 0 4 5
## 2 2 7 7 1 2 6 3 2 0 5
## 3 7 2 7 0 2 9 0 4 5 0
## 4 5 4 9 0 0 7 2 1 5 3
## 5 4 5 6 2 1 5 4 0 0 9
## 6 9 0 5 3 3 9 0 5 2 3
## MINK7512 MINK123M MINKGEM MKOOPKLA PWAPART PWABEDR PWALAND PPERSAUT PBESAUT
## 1 0 0 4 3 0 0 0 6 0
## 2 2 0 5 4 2 0 0 0 0
## 3 0 0 3 4 2 0 0 6 0
## 4 0 0 4 4 0 0 0 6 0
## 5 0 0 6 3 0 0 0 0 0
## 6 0 0 3 3 0 0 0 6 0
## PMOTSCO PVRAAUT PAANHANG PTRACTOR PWERKT PBROM PLEVEN PPERSONG PGEZONG
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## PWAOREG PBRAND PZEILPL PPLEZIER PFIETS PINBOED PBYSTAND AWAPART AWABEDR
## 1 0 5 0 0 0 0 0 0 0
## 2 0 2 0 0 0 0 0 2 0
## 3 0 2 0 0 0 0 0 1 0
## 4 0 2 0 0 0 0 0 0 0
## 5 0 6 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## AWALAND APERSAUT ABESAUT AMOTSCO AVRAAUT AAANHANG ATRACTOR AWERKT ABROM
## 1 0 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 1 0 0 0 0 0 0 0
## 4 0 1 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 1 0 0 0 0 0 0 0
## ALEVEN APERSONG AGEZONG AWAOREG ABRAND AZEILPL APLEZIER AFIETS AINBOED
## 1 0 0 0 0 1 0 0 0 0
## 2 0 0 0 0 1 0 0 0 0
## 3 0 0 0 0 1 0 0 0 0
## 4 0 0 0 0 1 0 0 0 0
## 5 0 0 0 0 1 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## ABYSTAND Purchase
## 1 0 No
## 2 0 No
## 3 0 No
## 4 0 No
## 5 0 No
## 6 0 Nostr(Caravan)## 'data.frame': 5822 obs. of 86 variables:
## $ MOSTYPE : num 33 37 37 9 40 23 39 33 33 11 ...
## $ MAANTHUI: num 1 1 1 1 1 1 2 1 1 2 ...
## $ MGEMOMV : num 3 2 2 3 4 2 3 2 2 3 ...
## $ MGEMLEEF: num 2 2 2 3 2 1 2 3 4 3 ...
## $ MOSHOOFD: num 8 8 8 3 10 5 9 8 8 3 ...
## $ MGODRK : num 0 1 0 2 1 0 2 0 0 3 ...
## $ MGODPR : num 5 4 4 3 4 5 2 7 1 5 ...
## $ MGODOV : num 1 1 2 2 1 0 0 0 3 0 ...
## $ MGODGE : num 3 4 4 4 4 5 5 2 6 2 ...
## $ MRELGE : num 7 6 3 5 7 0 7 7 6 7 ...
## $ MRELSA : num 0 2 2 2 1 6 2 2 0 0 ...
## $ MRELOV : num 2 2 4 2 2 3 0 0 3 2 ...
## $ MFALLEEN: num 1 0 4 2 2 3 0 0 3 2 ...
## $ MFGEKIND: num 2 4 4 3 4 5 3 5 3 2 ...
## $ MFWEKIND: num 6 5 2 4 4 2 6 4 3 6 ...
## $ MOPLHOOG: num 1 0 0 3 5 0 0 0 0 0 ...
## $ MOPLMIDD: num 2 5 5 4 4 5 4 3 1 4 ...
## $ MOPLLAAG: num 7 4 4 2 0 4 5 6 8 5 ...
## $ MBERHOOG: num 1 0 0 4 0 2 0 2 1 2 ...
## $ MBERZELF: num 0 0 0 0 5 0 0 0 1 0 ...
## $ MBERBOER: num 1 0 0 0 4 0 0 0 0 0 ...
## $ MBERMIDD: num 2 5 7 3 0 4 4 2 1 3 ...
## $ MBERARBG: num 5 0 0 1 0 2 1 5 8 3 ...
## $ MBERARBO: num 2 4 2 2 0 2 5 2 1 3 ...
## $ MSKA : num 1 0 0 3 9 2 0 2 1 1 ...
## $ MSKB1 : num 1 2 5 2 0 2 1 1 1 2 ...
## $ MSKB2 : num 2 3 0 1 0 2 4 2 0 1 ...
## $ MSKC : num 6 5 4 4 0 4 5 5 8 4 ...
## $ MSKD : num 1 0 0 0 0 2 0 2 1 2 ...
## $ MHHUUR : num 1 2 7 5 4 9 6 0 9 0 ...
## $ MHKOOP : num 8 7 2 4 5 0 3 9 0 9 ...
## $ MAUT1 : num 8 7 7 9 6 5 8 4 5 6 ...
## $ MAUT2 : num 0 1 0 0 2 3 0 4 2 1 ...
## $ MAUT0 : num 1 2 2 0 1 3 1 2 3 2 ...
## $ MZFONDS : num 8 6 9 7 5 9 9 6 7 6 ...
## $ MZPART : num 1 3 0 2 4 0 0 3 2 3 ...
## $ MINKM30 : num 0 2 4 1 0 5 4 2 7 2 ...
## $ MINK3045: num 4 0 5 5 0 2 3 5 2 3 ...
## $ MINK4575: num 5 5 0 3 9 3 3 3 1 3 ...
## $ MINK7512: num 0 2 0 0 0 0 0 0 0 1 ...
## $ MINK123M: num 0 0 0 0 0 0 0 0 0 0 ...
## $ MINKGEM : num 4 5 3 4 6 3 3 3 2 4 ...
## $ MKOOPKLA: num 3 4 4 4 3 3 5 3 3 7 ...
## $ PWAPART : num 0 2 2 0 0 0 0 0 0 2 ...
## $ PWABEDR : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PWALAND : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PPERSAUT: num 6 0 6 6 0 6 6 0 5 0 ...
## $ PBESAUT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PMOTSCO : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PVRAAUT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PAANHANG: num 0 0 0 0 0 0 0 0 0 0 ...
## $ PTRACTOR: num 0 0 0 0 0 0 0 0 0 0 ...
## $ PWERKT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PBROM : num 0 0 0 0 0 0 0 3 0 0 ...
## $ PLEVEN : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PPERSONG: num 0 0 0 0 0 0 0 0 0 0 ...
## $ PGEZONG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PWAOREG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PBRAND : num 5 2 2 2 6 0 0 0 0 3 ...
## $ PZEILPL : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PPLEZIER: num 0 0 0 0 0 0 0 0 0 0 ...
## $ PFIETS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PINBOED : num 0 0 0 0 0 0 0 0 0 0 ...
## $ PBYSTAND: num 0 0 0 0 0 0 0 0 0 0 ...
## $ AWAPART : num 0 2 1 0 0 0 0 0 0 1 ...
## $ AWABEDR : num 0 0 0 0 0 0 0 0 0 0 ...
## $ AWALAND : num 0 0 0 0 0 0 0 0 0 0 ...
## $ APERSAUT: num 1 0 1 1 0 1 1 0 1 0 ...
## $ ABESAUT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ AMOTSCO : num 0 0 0 0 0 0 0 0 0 0 ...
## $ AVRAAUT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ AAANHANG: num 0 0 0 0 0 0 0 0 0 0 ...
## $ ATRACTOR: num 0 0 0 0 0 0 0 0 0 0 ...
## $ AWERKT : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ABROM : num 0 0 0 0 0 0 0 1 0 0 ...
## $ ALEVEN : num 0 0 0 0 0 0 0 0 0 0 ...
## $ APERSONG: num 0 0 0 0 0 0 0 0 0 0 ...
## $ AGEZONG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ AWAOREG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ABRAND : num 1 1 1 1 1 0 0 0 0 1 ...
## $ AZEILPL : num 0 0 0 0 0 0 0 0 0 0 ...
## $ APLEZIER: num 0 0 0 0 0 0 0 0 0 0 ...
## $ AFIETS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ AINBOED : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ABYSTAND: num 0 0 0 0 0 0 0 0 0 0 ...
## $ Purchase: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...- 보험사의 실제 고객관련 데이터
- 5822개 관측치의 86개의 변수들로 이루어짐.
summary(Caravan)## MOSTYPE MAANTHUI MGEMOMV MGEMLEEF
## Min. : 1.00 Min. : 1.000 Min. :1.000 Min. :1.000
## 1st Qu.:10.00 1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :30.00 Median : 1.000 Median :3.000 Median :3.000
## Mean :24.25 Mean : 1.111 Mean :2.679 Mean :2.991
## 3rd Qu.:35.00 3rd Qu.: 1.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :41.00 Max. :10.000 Max. :5.000 Max. :6.000
## MOSHOOFD MGODRK MGODPR MGODOV
## Min. : 1.000 Min. :0.0000 Min. :0.000 Min. :0.00
## 1st Qu.: 3.000 1st Qu.:0.0000 1st Qu.:4.000 1st Qu.:0.00
## Median : 7.000 Median :0.0000 Median :5.000 Median :1.00
## Mean : 5.774 Mean :0.6965 Mean :4.627 Mean :1.07
## 3rd Qu.: 8.000 3rd Qu.:1.0000 3rd Qu.:6.000 3rd Qu.:2.00
## Max. :10.000 Max. :9.0000 Max. :9.000 Max. :5.00
## MGODGE MRELGE MRELSA MRELOV
## Min. :0.000 Min. :0.000 Min. :0.0000 Min. :0.00
## 1st Qu.:2.000 1st Qu.:5.000 1st Qu.:0.0000 1st Qu.:1.00
## Median :3.000 Median :6.000 Median :1.0000 Median :2.00
## Mean :3.259 Mean :6.183 Mean :0.8835 Mean :2.29
## 3rd Qu.:4.000 3rd Qu.:7.000 3rd Qu.:1.0000 3rd Qu.:3.00
## Max. :9.000 Max. :9.000 Max. :7.0000 Max. :9.00
## MFALLEEN MFGEKIND MFWEKIND MOPLHOOG MOPLMIDD
## Min. :0.000 Min. :0.00 Min. :0.0 Min. :0.000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:2.00 1st Qu.:3.0 1st Qu.:0.000 1st Qu.:2.000
## Median :2.000 Median :3.00 Median :4.0 Median :1.000 Median :3.000
## Mean :1.888 Mean :3.23 Mean :4.3 Mean :1.461 Mean :3.351
## 3rd Qu.:3.000 3rd Qu.:4.00 3rd Qu.:6.0 3rd Qu.:2.000 3rd Qu.:4.000
## Max. :9.000 Max. :9.00 Max. :9.0 Max. :9.000 Max. :9.000
## MOPLLAAG MBERHOOG MBERZELF MBERBOER
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:3.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.0000
## Median :5.000 Median :2.000 Median :0.000 Median :0.0000
## Mean :4.572 Mean :1.895 Mean :0.398 Mean :0.5223
## 3rd Qu.:6.000 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :9.000 Max. :9.000 Max. :5.000 Max. :9.0000
## MBERMIDD MBERARBG MBERARBO MSKA MSKB1
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:0.000 1st Qu.:1.000
## Median :3.000 Median :2.00 Median :2.000 Median :1.000 Median :2.000
## Mean :2.899 Mean :2.22 Mean :2.306 Mean :1.621 Mean :1.607
## 3rd Qu.:4.000 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :9.000 Max. :9.00 Max. :9.000 Max. :9.000 Max. :9.000
## MSKB2 MSKC MSKD MHHUUR
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:0.000 1st Qu.:2.000
## Median :2.000 Median :4.000 Median :1.000 Median :4.000
## Mean :2.203 Mean :3.759 Mean :1.067 Mean :4.237
## 3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:2.000 3rd Qu.:7.000
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000
## MHKOOP MAUT1 MAUT2 MAUT0 MZFONDS
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:5.00 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:5.000
## Median :5.000 Median :6.00 Median :1.000 Median :2.000 Median :7.000
## Mean :4.772 Mean :6.04 Mean :1.316 Mean :1.959 Mean :6.277
## 3rd Qu.:7.000 3rd Qu.:7.00 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:8.000
## Max. :9.000 Max. :9.00 Max. :7.000 Max. :9.000 Max. :9.000
## MZPART MINKM30 MINK3045 MINK4575
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :4.000 Median :3.000
## Mean :2.729 Mean :2.574 Mean :3.536 Mean :2.731
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :9.000 Max. :9.000 Max. :9.000 Max. :9.000
## MINK7512 MINK123M MINKGEM MKOOPKLA
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:3.000
## Median :0.0000 Median :0.0000 Median :4.000 Median :4.000
## Mean :0.7961 Mean :0.2027 Mean :3.784 Mean :4.236
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:4.000 3rd Qu.:6.000
## Max. :9.0000 Max. :9.0000 Max. :9.000 Max. :8.000
## PWAPART PWABEDR PWALAND PPERSAUT
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.00
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00
## Median :0.0000 Median :0.00000 Median :0.00000 Median :5.00
## Mean :0.7712 Mean :0.04002 Mean :0.07162 Mean :2.97
## 3rd Qu.:2.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:6.00
## Max. :3.0000 Max. :6.00000 Max. :4.00000 Max. :8.00
## PBESAUT PMOTSCO PVRAAUT PAANHANG
## Min. :0.00000 Min. :0.0000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.00000 Median :0.0000 Median :0.000000 Median :0.00000
## Mean :0.04827 Mean :0.1754 Mean :0.009447 Mean :0.02096
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :7.00000 Max. :7.0000 Max. :9.000000 Max. :5.00000
## PTRACTOR PWERKT PBROM PLEVEN
## Min. :0.00000 Min. :0.00000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.00000 Median :0.00000 Median :0.000 Median :0.0000
## Mean :0.09258 Mean :0.01305 Mean :0.215 Mean :0.1948
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :6.00000 Max. :6.00000 Max. :6.000 Max. :9.0000
## PPERSONG PGEZONG PWAOREG PBRAND
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :2.000
## Mean :0.01374 Mean :0.01529 Mean :0.02353 Mean :1.828
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:4.000
## Max. :6.00000 Max. :3.00000 Max. :7.00000 Max. :8.000
## PZEILPL PPLEZIER PFIETS PINBOED
## Min. :0.0000000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.0008588 Mean :0.01889 Mean :0.02525 Mean :0.01563
## 3rd Qu.:0.0000000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :3.0000000 Max. :6.00000 Max. :1.00000 Max. :6.00000
## PBYSTAND AWAPART AWABEDR AWALAND
## Min. :0.00000 Min. :0.000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.000 Median :0.00000 Median :0.00000
## Mean :0.04758 Mean :0.403 Mean :0.01477 Mean :0.02061
## 3rd Qu.:0.00000 3rd Qu.:1.000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :5.00000 Max. :2.000 Max. :5.00000 Max. :1.00000
## APERSAUT ABESAUT AMOTSCO AVRAAUT
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.000000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000000
## Median :1.0000 Median :0.00000 Median :0.00000 Median :0.000000
## Mean :0.5622 Mean :0.01048 Mean :0.04105 Mean :0.002233
## 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000000
## Max. :7.0000 Max. :4.00000 Max. :8.00000 Max. :3.000000
## AAANHANG ATRACTOR AWERKT ABROM
## Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.000000 Median :0.00000
## Mean :0.01254 Mean :0.03367 Mean :0.006183 Mean :0.07042
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :3.00000 Max. :4.00000 Max. :6.000000 Max. :2.00000
## ALEVEN APERSONG AGEZONG AWAOREG
## Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0.000000
## 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.000000
## Median :0.00000 Median :0.000000 Median :0.000000 Median :0.000000
## Mean :0.07661 Mean :0.005325 Mean :0.006527 Mean :0.004638
## 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.000000
## Max. :8.00000 Max. :1.000000 Max. :1.000000 Max. :2.000000
## ABRAND AZEILPL APLEZIER AFIETS
## Min. :0.0000 Min. :0.0000000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :1.0000 Median :0.0000000 Median :0.000000 Median :0.00000
## Mean :0.5701 Mean :0.0005153 Mean :0.006012 Mean :0.03178
## 3rd Qu.:1.0000 3rd Qu.:0.0000000 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :7.0000 Max. :1.0000000 Max. :2.000000 Max. :3.00000
## AINBOED ABYSTAND Purchase
## Min. :0.000000 Min. :0.00000 No :5474
## 1st Qu.:0.000000 1st Qu.:0.00000 Yes: 348
## Median :0.000000 Median :0.00000
## Mean :0.007901 Mean :0.01426
## 3rd Qu.:0.000000 3rd Qu.:0.00000
## Max. :2.000000 Max. :2.00000(a) Create a training set containing of the first of 1,000 observations, and a test set consisting the remaining observations.
train_cara<-1:1000
Caravan$Purchase <- ifelse(Caravan$Purchase == "Yes", 1, 0)
cara_train<-Caravan[train_cara, ]
cara_test<-Caravan[-train_cara, ](b) Fit a boosting model to the training set with Purchase as the response and the other variables as predictors. Use 1,000 trees, and a shrinkage value of 0.01. Which predictors appear to be the most important?
set.seed(12)
boost_cara<-gbm(Purchase~., data=cara_train, n.trees=1000,shrinkage=0.01,distribution="bernoulli")## Warning in gbm.fit(x = x, y = y, offset = offset, distribution = distribution,
## : variable 50: PVRAAUT has no variation.## Warning in gbm.fit(x = x, y = y, offset = offset, distribution = distribution,
## : variable 71: AVRAAUT has no variation.summary(boost_cara)
## var rel.inf
## PPERSAUT PPERSAUT 14.67025405
## MKOOPKLA MKOOPKLA 9.54133004
## MOPLHOOG MOPLHOOG 7.66893638
## PBRAND PBRAND 5.47273128
## MBERMIDD MBERMIDD 5.17699501
## MGODGE MGODGE 4.42889537
## MINK3045 MINK3045 4.33718805
## ABRAND ABRAND 4.05438295
## MOSTYPE MOSTYPE 2.90555001
## MBERARBG MBERARBG 2.71083209
## MGODPR MGODPR 2.20365115
## MINKGEM MINKGEM 2.20316619
## MAUT1 MAUT1 1.97344350
## MSKC MSKC 1.95990912
## PWAPART PWAPART 1.93799753
## MAUT2 MAUT2 1.89680874
## MFWEKIND MFWEKIND 1.70853273
## MGODOV MGODOV 1.64421839
## MRELGE MRELGE 1.51521134
## PBYSTAND PBYSTAND 1.50102845
## MSKA MSKA 1.49113907
## MINK7512 MINK7512 1.41507982
## MSKB1 MSKB1 1.40735434
## MBERHOOG MBERHOOG 1.35420148
## MINKM30 MINKM30 1.00769014
## MHHUUR MHHUUR 0.92257675
## MOPLMIDD MOPLMIDD 0.90793113
## MRELOV MRELOV 0.90481253
## MOSHOOFD MOSHOOFD 0.88872203
## APERSAUT APERSAUT 0.84888328
## MGODRK MGODRK 0.83135016
## MBERARBO MBERARBO 0.81614044
## MSKD MSKD 0.79575136
## MZFONDS MZFONDS 0.77626096
## MAUT0 MAUT0 0.74387395
## MINK4575 MINK4575 0.62552433
## MBERBOER MBERBOER 0.61049958
## MFGEKIND MFGEKIND 0.57930301
## PLEVEN PLEVEN 0.55820530
## MSKB2 MSKB2 0.54365763
## MGEMLEEF MGEMLEEF 0.52876860
## MHKOOP MHKOOP 0.38191647
## MGEMOMV MGEMOMV 0.33816702
## MRELSA MRELSA 0.25353281
## MFALLEEN MFALLEEN 0.23194727
## MBERZELF MBERZELF 0.23125165
## PMOTSCO PMOTSCO 0.16674643
## MINK123M MINK123M 0.14096277
## MOPLLAAG MOPLLAAG 0.11906770
## MZPART MZPART 0.06761961
## MAANTHUI MAANTHUI 0.00000000
## PWABEDR PWABEDR 0.00000000
## PWALAND PWALAND 0.00000000
## PBESAUT PBESAUT 0.00000000
## PVRAAUT PVRAAUT 0.00000000
## PAANHANG PAANHANG 0.00000000
## PTRACTOR PTRACTOR 0.00000000
## PWERKT PWERKT 0.00000000
## PBROM PBROM 0.00000000
## PPERSONG PPERSONG 0.00000000
## PGEZONG PGEZONG 0.00000000
## PWAOREG PWAOREG 0.00000000
## PZEILPL PZEILPL 0.00000000
## PPLEZIER PPLEZIER 0.00000000
## PFIETS PFIETS 0.00000000
## PINBOED PINBOED 0.00000000
## AWAPART AWAPART 0.00000000
## AWABEDR AWABEDR 0.00000000
## AWALAND AWALAND 0.00000000
## ABESAUT ABESAUT 0.00000000
## AMOTSCO AMOTSCO 0.00000000
## AVRAAUT AVRAAUT 0.00000000
## AAANHANG AAANHANG 0.00000000
## ATRACTOR ATRACTOR 0.00000000
## AWERKT AWERKT 0.00000000
## ABROM ABROM 0.00000000
## ALEVEN ALEVEN 0.00000000
## APERSONG APERSONG 0.00000000
## AGEZONG AGEZONG 0.00000000
## AWAOREG AWAOREG 0.00000000
## AZEILPL AZEILPL 0.00000000
## APLEZIER APLEZIER 0.00000000
## AFIETS AFIETS 0.00000000
## AINBOED AINBOED 0.00000000
## ABYSTAND ABYSTAND 0.00000000PPERSAUT, MKOOPKLA와 MOPLHOOGrk가 가장 중요한 것으로 나온다.