::p_load("data.table",
pacman"tidyverse",
"dplyr", "tidyr",
"ggplot2", "GGally",
"caret",
"glmnet") # For glmnet
<- fread("../Titanic.csv") # 데이터 불러오기
titanic
%>%
titanic as_tibble
12 Ridge Regression
Ridge Regression의 장점
- 규제항을 통해 회귀계수를 “0”에 가깝게 추정한다.
- 회귀계수 추정치의 분산을 감소시켜
Training Dataset
의 변화에도 회귀계수 추정치가 크게 변하지 않는다.
- 회귀계수 추정치의 분산을 감소시켜
Ridge Regression의 단점
- \(\lambda = \infty\)가 아닌 한 회귀계수를 정확하게 “0”으로 추정하지 못한다.
- 변수 선택을 수행할 수 없다.
- 예측변수가 많을 경우 해석이 어렵다.
실습 자료 : 1912년 4월 15일 타이타닉호 침몰 당시 탑승객들의 정보를 기록한 데이터셋이며, 총 11개의 변수를 포함하고 있다. 이 자료에서 Target은
Survived
이다.


12.1 데이터 불러오기
# A tibble: 891 × 11
Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
<int> <int> <chr> <chr> <dbl> <int> <int> <chr> <dbl> <chr> <chr>
1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 "" S
2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.3 "C85" C
3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.92 "" S
4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 "C123" S
5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 "" S
6 0 3 Moran, Mr. James male NA 0 0 330877 8.46 "" Q
7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.9 "E46" S
8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.1 "" S
9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1 "" S
10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.1 "" C
# ℹ 881 more rows
12.2 데이터 전처리 I
%<>%
titanic data.frame() %>% # Data Frame 형태로 변환
mutate(Survived = ifelse(Survived == 1, "yes", "no")) # Target을 문자형 변수로 변환
# 1. Convert to Factor
<- c("Pclass", "Sex",
fac.col # Target
"Survived")
<- titanic %>%
titanic mutate_at(fac.col, as.factor) # 범주형으로 변환
glimpse(titanic) # 데이터 구조 확인
Rows: 891
Columns: 11
$ Survived <fct> no, yes, yes, yes, no, no, no, no, yes, yes, yes, yes, no, no, no, yes, no, yes, no, yes, no, yes, yes, yes, no, yes, no, no, yes, no, no, yes, yes, no, no, no, yes, no, no, yes, no…
$ Pclass <fct> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 1, 3, 3, 3, 1, 3, 3, 1, 1, 3, 2, 1, 1, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, 2, 1, 1, 2, 3, 2, 3, 3…
$ Name <chr> "Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley (Florence Briggs Thayer)", "Heikkinen, Miss. Laina", "Futrelle, Mrs. Jacques Heath (Lily May Peel)", "Allen, Mr. William Henry…
$ Sex <fct> male, female, female, female, male, male, male, male, female, female, female, female, male, male, female, female, male, male, female, female, male, male, female, male, female, femal…
$ Age <dbl> 22.0, 38.0, 26.0, 35.0, 35.0, NA, 54.0, 2.0, 27.0, 14.0, 4.0, 58.0, 20.0, 39.0, 14.0, 55.0, 2.0, NA, 31.0, NA, 35.0, 34.0, 15.0, 28.0, 8.0, 38.0, NA, 19.0, NA, NA, 40.0, NA, NA, 66.…
$ SibSp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1, 0, 0, 0, 0, 0, 3, 1, 0, 3, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 2, 1, 1, 1, 0, 1, 0, 0, 1, 0, 2, 1, 4, 0, 1, 1, 0, 0, 0, 0, 1, 5, 0…
$ Parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 5, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 2, 0…
$ Ticket <chr> "A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", "373450", "330877", "17463", "349909", "347742", "237736", "PP 9549", "113783", "A/5. 2151", "347082", "350406", "248706", "38…
$ Fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.8625, 21.0750, 11.1333, 30.0708, 16.7000, 26.5500, 8.0500, 31.2750, 7.8542, 16.0000, 29.1250, 13.0000, 18.0000, 7.2250, 26.0000,…
$ Cabin <chr> "", "C85", "", "C123", "", "", "E46", "", "", "", "G6", "C103", "", "", "", "", "", "", "", "", "", "D56", "", "A6", "", "", "", "C23 C25 C27", "", "", "", "B78", "", "", "", "", ""…
$ Embarked <chr> "S", "C", "S", "S", "S", "Q", "S", "S", "S", "C", "S", "S", "S", "S", "S", "S", "Q", "S", "S", "C", "S", "S", "Q", "S", "S", "S", "C", "S", "Q", "S", "C", "C", "Q", "S", "C", "S", "…
# 2. Generate New Variable
<- titanic %>%
titanic mutate(FamSize = SibSp + Parch) # "FamSize = 형제 및 배우자 수 + 부모님 및 자녀 수"로 가족 수를 의미하는 새로운 변수
glimpse(titanic) # 데이터 구조 확인
Rows: 891
Columns: 12
$ Survived <fct> no, yes, yes, yes, no, no, no, no, yes, yes, yes, yes, no, no, no, yes, no, yes, no, yes, no, yes, yes, yes, no, yes, no, no, yes, no, no, yes, yes, no, no, no, yes, no, no, yes, no…
$ Pclass <fct> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3, 2, 3, 3, 2, 2, 3, 1, 3, 3, 3, 1, 3, 3, 1, 1, 3, 2, 1, 1, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, 2, 1, 1, 2, 3, 2, 3, 3…
$ Name <chr> "Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley (Florence Briggs Thayer)", "Heikkinen, Miss. Laina", "Futrelle, Mrs. Jacques Heath (Lily May Peel)", "Allen, Mr. William Henry…
$ Sex <fct> male, female, female, female, male, male, male, male, female, female, female, female, male, male, female, female, male, male, female, female, male, male, female, male, female, femal…
$ Age <dbl> 22.0, 38.0, 26.0, 35.0, 35.0, NA, 54.0, 2.0, 27.0, 14.0, 4.0, 58.0, 20.0, 39.0, 14.0, 55.0, 2.0, NA, 31.0, NA, 35.0, 34.0, 15.0, 28.0, 8.0, 38.0, NA, 19.0, NA, NA, 40.0, NA, NA, 66.…
$ SibSp <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4, 0, 1, 0, 0, 0, 0, 0, 3, 1, 0, 3, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 2, 1, 1, 1, 0, 1, 0, 0, 1, 0, 2, 1, 4, 0, 1, 1, 0, 0, 0, 0, 1, 5, 0…
$ Parch <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 5, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 2, 2, 0…
$ Ticket <chr> "A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", "373450", "330877", "17463", "349909", "347742", "237736", "PP 9549", "113783", "A/5. 2151", "347082", "350406", "248706", "38…
$ Fare <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, 51.8625, 21.0750, 11.1333, 30.0708, 16.7000, 26.5500, 8.0500, 31.2750, 7.8542, 16.0000, 29.1250, 13.0000, 18.0000, 7.2250, 26.0000,…
$ Cabin <chr> "", "C85", "", "C123", "", "", "E46", "", "", "", "G6", "C103", "", "", "", "", "", "", "", "", "", "D56", "", "A6", "", "", "", "C23 C25 C27", "", "", "", "B78", "", "", "", "", ""…
$ Embarked <chr> "S", "C", "S", "S", "S", "Q", "S", "S", "S", "C", "S", "S", "S", "S", "S", "S", "Q", "S", "S", "C", "S", "S", "Q", "S", "S", "S", "C", "S", "Q", "S", "C", "C", "Q", "S", "C", "S", "…
$ FamSize <int> 1, 1, 0, 1, 0, 0, 0, 4, 2, 1, 2, 0, 0, 6, 0, 0, 5, 0, 1, 0, 0, 0, 0, 0, 4, 6, 0, 5, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 2, 1, 1, 1, 0, 3, 0, 0, 1, 0, 2, 1, 5, 0, 1, 1, 1, 0, 0, 0, 3, 7, 0…
# 3. Select Variables used for Analysis
<- titanic %>%
titanic1 select(Survived, Pclass, Sex, Age, Fare, FamSize) # 분석에 사용할 변수 선택
%>%
titanic1 as_tibble
# A tibble: 891 × 6
Survived Pclass Sex Age Fare FamSize
<fct> <fct> <fct> <dbl> <dbl> <int>
1 no 3 male 22 7.25 1
2 yes 1 female 38 71.3 1
3 yes 3 female 26 7.92 0
4 yes 1 female 35 53.1 1
5 no 3 male 35 8.05 0
6 no 3 male NA 8.46 0
7 no 1 male 54 51.9 0
8 no 3 male 2 21.1 4
9 yes 3 female 27 11.1 2
10 yes 2 female 14 30.1 1
# ℹ 881 more rows
12.3 데이터 탐색
ggpairs(titanic1,
aes(colour = Survived)) + # Target의 범주에 따라 색깔을 다르게 표현
theme_bw()
ggpairs(titanic1,
aes(colour = Survived, alpha = 0.8)) + # Target의 범주에 따라 색깔을 다르게 표현
scale_colour_manual(values = c("#E69F00", "#56B4E9")) + # 특정 색깔 지정
scale_fill_manual(values = c("#E69F00", "#56B4E9")) + # 특정 색깔 지정
theme_bw()
12.4 데이터 분할
# Partition (Training Dataset : Test Dataset = 7:3)
<- titanic1$Survived # Target
y
set.seed(200)
<- createDataPartition(y, p = 0.7, list =T) # Index를 이용하여 7:3으로 분할
ind <- titanic1[ind$Resample1,] # Training Dataset
titanic.trd <- titanic1[-ind$Resample1,] # Test Dataset titanic.ted
12.5 데이터 전처리 II
# 1. Imputation
<- titanic.trd %>%
titanic.trd.Imp mutate(Age = replace_na(Age, mean(Age, na.rm = TRUE))) # 평균으로 결측값 대체
<- titanic.ted %>%
titanic.ted.Imp mutate(Age = replace_na(Age, mean(titanic.trd$Age, na.rm = TRUE))) # Training Dataset을 이용하여 결측값 대체
# 2. Standardization
<- preProcess(titanic.trd.Imp,
preProcValues method = c("center", "scale")) # Standardization 정의 -> Training Dataset에 대한 평균과 표준편차 계산
<- predict(preProcValues, titanic.trd.Imp) # Standardization for Training Dataset
titanic.trd.Imp <- predict(preProcValues, titanic.ted.Imp) # Standardization for Test Dataset
titanic.ted.Imp
# 3. Convert Factor Var. into Dummy Var.
<- model.matrix(Survived ~., # Survived는 Target으로 제외
train.x -1] # [,-1] : 절편 제거
titanic.trd.Imp)[,
train.x
Pclass2 Pclass3 Sexmale Age Fare FamSize
1 0 1 1 -0.61306970 -0.517763935 0.04506631
3 0 1 0 -0.30411628 -0.504633254 -0.55421976
4 0 0 0 0.39102893 0.374149700 0.04506631
5 0 1 1 0.39102893 -0.502201647 -0.55421976
6 0 1 1 0.00000000 -0.494259044 -0.55421976
8 0 1 1 -2.15783684 -0.248828144 1.84292454
9 0 1 0 -0.22687792 -0.442222643 0.64435239
10 1 0 0 -1.23097656 -0.073834105 0.04506631
11 0 1 0 -2.00336012 -0.333934407 0.64435239
12 0 0 0 2.16751113 -0.142323735 -0.55421976
14 0 1 1 0.69998236 -0.050408971 3.04149669
15 0 1 0 -1.23097656 -0.506010517 -0.55421976
18 1 0 1 0.00000000 -0.405909990 -0.55421976
19 0 1 0 0.08207551 -0.308645689 0.04506631
20 0 1 0 0.00000000 -0.518250257 -0.55421976
21 1 0 1 0.39102893 -0.153022808 -0.55421976
24 0 0 1 -0.14963956 0.031779363 -0.55421976
25 0 1 0 -1.69440670 -0.248828144 1.84292454
27 0 1 1 0.00000000 -0.518250257 -0.55421976
28 0 0 1 -0.84478477 4.457305033 2.44221062
29 0 1 0 0.00000000 -0.505524195 -0.55421976
30 0 1 1 0.00000000 -0.505201278 -0.55421976
31 0 0 1 0.77722072 -0.119548327 -0.55421976
32 0 0 0 0.00000000 2.191451452 0.04506631
33 0 1 0 0.00000000 -0.508037505 -0.55421976
34 1 0 1 2.78541799 -0.454542140 -0.55421976
35 0 0 1 -0.14963956 0.939659905 0.04506631
36 0 0 1 0.93169743 0.352751554 0.04506631
38 0 1 1 -0.69030806 -0.502201647 -0.55421976
39 0 1 0 -0.92202313 -0.308645689 0.64435239
40 0 1 0 -1.23097656 -0.440113953 0.04506631
41 0 1 0 0.77722072 -0.474481321 0.04506631
42 1 0 0 -0.22687792 -0.250287109 0.04506631
43 0 1 1 0.00000000 -0.505201278 -0.55421976
45 0 1 0 -0.84478477 -0.505524195 -0.55421976
47 0 1 1 0.00000000 -0.357277839 0.04506631
48 0 1 0 0.00000000 -0.508037505 -0.55421976
49 0 1 1 0.00000000 -0.237074726 0.64435239
50 0 1 0 -0.92202313 -0.312536261 0.04506631
51 0 1 1 -1.77164505 0.113238214 2.44221062
53 0 0 0 1.47236593 0.833805222 0.04506631
55 0 0 1 2.70817963 0.546875535 0.04506631
56 0 0 1 0.00000000 0.031779363 -0.55421976
60 0 1 1 -1.46269163 0.253541968 3.64078277
61 0 1 1 -0.61306970 -0.518168555 -0.55421976
62 0 0 0 0.62274400 0.897431637 -0.55421976
64 0 1 1 -2.00336012 -0.116062374 2.44221062
65 0 0 1 0.00000000 -0.119548327 -0.55421976
70 0 1 1 -0.30411628 -0.490286770 0.64435239
72 0 1 0 -1.07649984 0.253541968 3.64078277
74 0 1 1 -0.30411628 -0.377621640 0.04506631
75 0 1 1 0.15931386 0.440207722 -0.55421976
76 0 1 1 -0.38135463 -0.509982791 -0.55421976
77 0 1 1 0.00000000 -0.505201278 -0.55421976
79 1 0 1 -2.24820571 -0.094664228 0.64435239
80 0 1 0 0.00483715 -0.416122741 -0.55421976
82 0 1 1 -0.07240121 -0.473995000 -0.55421976
83 0 1 0 0.00000000 -0.507308023 -0.55421976
84 0 0 1 -0.14963956 0.257432540 -0.55421976
85 1 0 0 -0.99926149 -0.454542140 -0.55421976
86 0 1 0 0.23655222 -0.350469338 1.24363847
88 0 1 1 0.00000000 -0.502201647 -0.55421976
89 0 0 0 -0.53583135 4.457305033 2.44221062
91 0 1 1 -0.07240121 -0.502201647 -0.55421976
92 0 1 1 -0.76754642 -0.506010517 -0.55421976
93 0 0 1 1.24065086 0.531231545 0.04506631
94 0 1 1 -0.30411628 -0.258554574 1.24363847
95 0 1 1 2.24474949 -0.517763935 -0.55421976
96 0 1 1 0.00000000 -0.502201647 -0.55421976
97 0 0 1 3.17160977 0.015326133 -0.55421976
103 0 0 1 -0.69030806 0.844665754 0.04506631
105 0 1 1 0.54550565 -0.504633254 0.64435239
107 0 1 0 -0.69030806 -0.509982791 -0.55421976
108 0 1 1 0.00000000 -0.507551184 -0.55421976
110 0 1 0 0.00000000 -0.189010600 0.04506631
111 0 0 1 1.31788921 0.352751554 -0.55421976
112 0 1 0 -1.19235738 -0.377621640 0.04506631
113 0 1 1 -0.61306970 -0.502201647 -0.55421976
114 0 1 0 -0.76754642 -0.467672820 0.04506631
115 0 1 0 -0.99926149 -0.377541884 -0.55421976
116 0 1 1 -0.69030806 -0.504633254 -0.55421976
117 0 1 1 3.13299059 -0.508037505 -0.55421976
119 0 0 1 -0.45859299 4.156190321 0.04506631
122 0 1 1 0.00000000 -0.502201647 -0.55421976
125 0 0 1 1.85855771 0.844665754 0.04506631
126 0 1 1 -1.38545327 -0.440113953 0.04506631
128 0 1 1 -0.45859299 -0.519870680 -0.55421976
129 0 1 0 0.00000000 -0.223864289 0.64435239
130 0 1 1 1.16341250 -0.523113472 -0.55421976
131 0 1 1 0.23655222 -0.505201278 -0.55421976
132 0 1 1 -0.76754642 -0.521654507 -0.55421976
134 1 0 0 -0.07240121 -0.153022808 0.04506631
136 1 0 1 -0.53583135 -0.366113328 -0.55421976
138 0 0 1 0.54550565 0.374149700 0.04506631
141 0 1 0 0.00000000 -0.362222756 0.64435239
142 0 1 0 -0.61306970 -0.508037505 -0.55421976
143 0 1 0 -0.45859299 -0.350469338 0.04506631
146 1 0 1 -0.84478477 0.056095438 0.64435239
147 0 1 1 -0.22687792 -0.507146564 -0.55421976
148 0 1 0 -1.61716834 0.009894895 1.84292454
149 1 0 1 0.50688647 -0.153022808 0.64435239
150 1 0 1 0.93169743 -0.405909990 -0.55421976
151 1 0 1 1.62684264 -0.415150098 -0.55421976
153 0 1 1 1.97441524 -0.502201647 -0.55421976
154 0 1 1 0.81583989 -0.376730699 0.64435239
156 0 0 1 1.62684264 0.535203819 0.04506631
157 0 1 0 -1.07649984 -0.508362368 -0.55421976
158 0 1 1 0.00483715 -0.502201647 -0.55421976
160 0 1 1 0.00000000 0.694149249 5.43864100
162 1 0 0 0.77722072 -0.352414624 -0.55421976
163 0 1 1 -0.30411628 -0.507551184 -0.55421976
164 0 1 1 -0.99926149 -0.490286770 -0.55421976
167 0 0 0 0.00000000 0.411110134 0.04506631
169 0 0 1 0.00000000 -0.154481773 -0.55421976
171 0 0 1 2.39922620 -0.007126358 -0.55421976
173 0 1 0 -2.23507519 -0.442222643 0.64435239
174 0 1 1 -0.69030806 -0.504633254 -0.55421976
176 0 1 1 -0.92202313 -0.506010517 0.64435239
179 1 0 1 0.00483715 -0.405909990 -0.55421976
180 0 1 1 0.46826729 -0.658797171 -0.55421976
181 0 1 0 0.00000000 0.694149249 5.43864100
182 1 0 1 0.00000000 -0.366031626 -0.55421976
184 1 0 1 -2.23507519 0.099864373 1.24363847
185 0 1 0 -2.00336012 -0.230347927 0.64435239
188 0 0 1 1.16341250 -0.142323735 -0.55421976
189 0 1 1 0.77722072 -0.357277839 0.64435239
190 0 1 1 0.46826729 -0.505201278 -0.55421976
191 1 0 0 0.15931386 -0.405909990 -0.55421976
192 1 0 1 -0.84478477 -0.405909990 -0.55421976
194 1 0 1 -2.08059848 -0.153022808 0.64435239
196 0 0 0 2.16751113 2.191451452 -0.55421976
197 0 1 1 0.00000000 -0.508037505 -0.55421976
198 0 1 1 0.93169743 -0.495311444 0.04506631
199 0 1 0 0.00000000 -0.508037505 -0.55421976
200 1 0 0 -0.45859299 -0.405909990 -0.55421976
202 0 1 1 0.00000000 0.694149249 5.43864100
203 0 1 1 0.31379058 -0.532435282 -0.55421976
204 0 1 1 1.20203168 -0.518250257 -0.55421976
205 0 1 1 -0.92202313 -0.502201647 -0.55421976
206 0 1 0 -2.15783684 -0.455271622 0.04506631
207 0 1 1 0.15931386 -0.350469338 0.04506631
209 0 1 0 -1.07649984 -0.508037505 -0.55421976
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692 0 1 0 -2.00336012 -0.397803983 0.04506631
693 0 1 1 0.00000000 0.440207722 -0.55421976
694 0 1 1 -0.38135463 -0.518250257 -0.55421976
695 0 0 1 2.32198785 -0.142323735 -0.55421976
699 0 0 1 1.47236593 1.498200151 0.64435239
701 0 0 0 -0.92202313 3.767214822 0.04506631
702 0 0 1 0.39102893 -0.147430111 -0.55421976
703 0 1 0 -0.92202313 -0.377621640 0.04506631
704 0 1 1 -0.38135463 -0.508198964 -0.55421976
705 0 1 1 -0.30411628 -0.506010517 0.04506631
708 0 0 1 0.93169743 -0.147430111 -0.55421976
709 0 0 0 -0.61306970 2.289283776 -0.55421976
710 0 1 1 0.00000000 -0.362222756 0.64435239
712 0 0 1 0.00000000 -0.142323735 -0.55421976
713 0 0 1 1.39512757 0.352751554 0.04506631
714 0 1 1 -0.07240121 -0.474319863 -0.55421976
716 0 1 1 -0.84478477 -0.509982791 -0.55421976
717 0 0 0 0.62274400 3.767214822 -0.55421976
718 1 0 0 -0.22687792 -0.454542140 -0.55421976
719 0 1 1 0.00000000 -0.357277839 -0.55421976
720 0 1 1 0.23655222 -0.507551184 -0.55421976
721 1 0 0 -1.84888341 -0.016852788 0.04506631
723 1 0 1 0.31379058 -0.405909990 -0.55421976
725 0 0 1 -0.22687792 0.374149700 0.04506631
726 0 1 1 -0.76754642 -0.490286770 -0.55421976
728 0 1 0 0.00000000 -0.508280666 -0.55421976
729 1 0 1 -0.38135463 -0.153022808 0.04506631
730 0 1 0 -0.38135463 -0.504633254 0.04506631
732 0 1 1 -1.46269163 -0.293326562 -0.55421976
735 1 0 1 -0.53583135 -0.405909990 -0.55421976
736 0 1 1 -0.11102039 -0.345606123 -0.55421976
737 0 1 0 1.39512757 0.009894895 1.84292454
738 0 0 1 0.39102893 9.307471078 -0.55421976
739 0 1 1 0.00000000 -0.505201278 -0.55421976
740 0 1 1 0.00000000 -0.505201278 -0.55421976
742 0 0 1 0.46826729 0.875060848 0.04506631
743 0 0 0 -0.69030806 4.445146996 1.84292454
744 0 1 1 -0.45859299 -0.345606123 0.04506631
745 0 1 1 0.08207551 -0.504633254 -0.55421976
749 0 0 1 -0.84478477 0.374149700 0.04506631
750 0 1 1 0.08207551 -0.508037505 -0.55421976
752 0 1 1 -1.84888341 -0.416122741 0.04506631
753 0 1 1 0.23655222 -0.473995000 -0.55421976
754 0 1 1 -0.53583135 -0.505201278 -0.55421976
755 1 0 0 1.39512757 0.605638735 1.24363847
757 0 1 1 -0.14963956 -0.507146564 -0.55421976
759 0 1 1 0.31379058 -0.502201647 -0.55421976
760 0 0 0 0.23655222 1.023875227 -0.55421976
762 0 1 1 0.85445907 -0.520195543 -0.55421976
763 0 1 1 -0.76754642 -0.518168555 -0.55421976
764 0 0 0 0.46826729 1.675546040 1.24363847
766 0 0 0 1.62684264 0.857714732 0.04506631
767 0 0 1 0.00000000 0.111536089 -0.55421976
768 0 1 0 0.04345633 -0.508037505 -0.55421976
769 0 1 1 0.00000000 -0.189010600 0.04506631
770 0 1 1 0.15931386 -0.496122628 -0.55421976
771 0 1 1 -0.45859299 -0.473995000 -0.55421976
773 1 0 0 2.09027278 -0.454542140 -0.55421976
774 0 1 1 0.00000000 -0.518250257 -0.55421976
777 0 1 1 0.00000000 -0.508037505 -0.55421976
778 0 1 0 -1.92612177 -0.416122741 -0.55421976
780 0 0 0 1.00893579 3.452321649 0.04506631
781 0 1 0 -1.30821491 -0.518168555 -0.55421976
782 0 0 0 -0.99926149 0.450015854 0.04506631
783 0 0 1 -0.07240121 -0.075211368 -0.55421976
784 0 1 1 0.00000000 -0.202627602 1.24363847
786 0 1 1 -0.38135463 -0.517763935 -0.55421976
789 0 1 1 -2.23507519 -0.258554574 1.24363847
790 0 0 1 1.24065086 0.881869349 -0.55421976
791 0 1 1 0.00000000 -0.508037505 -0.55421976
792 1 0 1 -1.07649984 -0.153022808 -0.55421976
793 0 1 0 0.00000000 0.694149249 5.43864100
794 0 0 1 0.00000000 -0.061676068 -0.55421976
795 0 1 1 -0.38135463 -0.505201278 -0.55421976
796 1 0 1 0.69998236 -0.405909990 -0.55421976
797 0 0 0 1.47236593 -0.154400071 -0.55421976
799 0 1 1 0.00483715 -0.518168555 -0.55421976
800 0 1 0 0.00483715 -0.189010600 0.64435239
801 1 0 1 0.31379058 -0.405909990 -0.55421976
803 0 0 1 -1.46269163 1.675546040 1.24363847
804 0 1 1 -2.27987344 -0.493122997 0.04506631
806 0 1 1 0.08207551 -0.507551184 -0.55421976
808 0 1 0 -0.92202313 -0.507551184 -0.55421976
811 0 1 1 -0.30411628 -0.505362737 -0.55421976
812 0 1 1 0.69998236 -0.189010600 -0.55421976
813 1 0 1 0.39102893 -0.454542140 -0.55421976
815 0 1 1 0.04345633 -0.502201647 -0.55421976
816 0 0 1 0.00000000 -0.658797171 -0.55421976
817 0 1 0 -0.53583135 -0.504633254 -0.55421976
819 0 1 1 1.00893579 -0.533326223 -0.55421976
822 0 1 1 -0.22687792 -0.490286770 -0.55421976
823 0 0 1 0.62274400 -0.658797171 -0.55421976
824 0 1 0 -0.22687792 -0.416122741 0.04506631
825 0 1 1 -2.15783684 0.113238214 2.44221062
826 0 1 1 0.00000000 -0.523599793 -0.55421976
827 0 1 1 0.00000000 0.440207722 -0.55421976
828 1 0 1 -2.23507519 0.061040355 0.64435239
831 0 1 0 -1.15373820 -0.377621640 0.04506631
832 1 0 1 -2.24820571 -0.294056044 0.64435239
833 0 1 1 0.00000000 -0.518168555 -0.55421976
835 0 1 1 -0.92202313 -0.497338432 -0.55421976
836 0 0 0 0.69998236 0.958869605 0.64435239
838 0 1 1 0.00000000 -0.502201647 -0.55421976
840 0 0 1 0.00000000 -0.081047226 -0.55421976
843 0 0 0 0.00483715 -0.055758508 -0.55421976
845 0 1 1 -0.99926149 -0.490286770 -0.55421976
846 0 1 1 0.93169743 -0.511928077 -0.55421976
847 0 1 1 0.00000000 0.694149249 5.43864100
849 1 0 1 -0.14963956 -0.016852788 0.04506631
850 0 0 0 0.00000000 1.074534365 0.04506631
851 0 1 1 -2.00336012 -0.050408971 3.04149669
852 0 1 1 3.40332484 -0.507551184 -0.55421976
853 0 1 0 -1.61716834 -0.362222756 0.64435239
855 1 0 0 1.08617414 -0.153022808 0.04506631
856 0 1 0 -0.92202313 -0.476912929 0.04506631
857 0 0 0 1.16341250 2.548331678 0.64435239
858 0 0 1 1.62684264 -0.142323735 -0.55421976
859 0 1 0 -0.45859299 -0.284168155 1.24363847
860 0 1 1 0.00000000 -0.518168555 -0.55421976
861 0 1 1 0.85445907 -0.384350385 0.64435239
862 1 0 1 -0.69030806 -0.435089280 0.04506631
864 0 1 0 0.00000000 0.694149249 5.43864100
865 1 0 1 -0.45859299 -0.405909990 -0.55421976
867 1 0 0 -0.22687792 -0.389213600 0.04506631
868 0 0 1 0.08207551 0.323490562 -0.55421976
871 0 1 1 -0.30411628 -0.505201278 -0.55421976
872 0 0 0 1.31788921 0.363532329 0.64435239
874 0 1 1 1.31788921 -0.483721430 -0.55421976
877 0 1 1 -0.76754642 -0.467268201 -0.55421976
878 0 1 1 -0.84478477 -0.505201278 -0.55421976
879 0 1 1 0.00000000 -0.505201278 -0.55421976
880 0 0 0 2.01303442 0.958869605 0.04506631
881 1 0 0 -0.38135463 -0.153022808 0.04506631
882 0 1 1 0.23655222 -0.505201278 -0.55421976
883 0 1 0 -0.61306970 -0.454217277 -0.55421976
885 0 1 1 -0.38135463 -0.521654507 -0.55421976
886 0 1 0 0.69998236 -0.092232621 2.44221062
887 1 0 1 -0.22687792 -0.405909990 -0.55421976
888 0 0 0 -0.84478477 -0.075211368 -0.55421976
889 0 1 0 0.00000000 -0.202627602 1.24363847
<- model.matrix(Survived ~., # Survived는 Target으로 제외
test.x -1] # [,-1] : 절편 제거
titanic.ted.Imp)[,
test.x
Pclass2 Pclass3 Sexmale Age Fare FamSize
2 0 0 0 0.62274400 0.727866891 0.04506631
7 0 0 1 1.85855771 0.350076786 -0.55421976
13 0 1 1 -0.76754642 -0.502201647 -0.55421976
16 1 0 0 1.93579607 -0.347551409 -0.55421976
17 0 1 1 -2.15783684 -0.092232621 2.44221062
22 1 0 1 0.31379058 -0.405909990 -0.55421976
23 0 1 0 -1.15373820 -0.502606266 -0.55421976
26 0 1 0 0.62274400 -0.048220525 3.04149669
37 0 1 1 0.00000000 -0.518168555 -0.55421976
44 1 0 0 -2.08059848 0.150037190 1.24363847
46 0 1 1 0.00000000 -0.502201647 -0.55421976
52 0 1 1 -0.69030806 -0.507064862 -0.55421976
54 1 0 0 -0.07240121 -0.153022808 0.04506631
57 1 0 0 -0.69030806 -0.454542140 -0.55421976
58 0 1 1 -0.11102039 -0.518168555 -0.55421976
59 1 0 0 -1.92612177 -0.118980303 1.24363847
63 0 0 1 1.16341250 0.965030325 0.04506631
66 0 1 1 0.00000000 -0.362222756 0.64435239
67 1 0 0 -0.07240121 -0.454542140 -0.55421976
68 0 1 1 -0.84478477 -0.500094902 -0.55421976
69 0 1 0 -0.99926149 -0.504633254 3.04149669
71 1 0 1 0.15931386 -0.454542140 -0.55421976
73 1 0 1 -0.69030806 0.770988046 -0.55421976
78 0 1 1 0.00000000 -0.502201647 -0.55421976
81 0 1 1 -0.61306970 -0.483721430 -0.55421976
87 0 1 1 -1.07649984 0.009894895 1.84292454
90 0 1 1 -0.45859299 -0.502201647 -0.55421976
98 0 0 1 -0.53583135 0.573702975 0.04506631
99 1 0 0 0.31379058 -0.211381389 0.04506631
100 1 0 1 0.31379058 -0.153022808 0.04506631
101 0 1 0 -0.14963956 -0.505201278 -0.55421976
102 0 1 1 0.00000000 -0.505201278 -0.55421976
104 0 1 1 0.23655222 -0.490448229 -0.55421976
106 0 1 1 -0.14963956 -0.505201278 -0.55421976
109 0 1 1 0.62274400 -0.505201278 -0.55421976
118 1 0 1 -0.07240121 -0.250287109 0.04506631
120 0 1 0 -2.15783684 -0.050408971 3.04149669
121 1 0 1 -0.69030806 0.770988046 0.64435239
123 1 0 1 0.19793304 -0.073834105 0.04506631
124 1 0 0 0.19793304 -0.405909990 -0.55421976
127 0 1 1 0.00000000 -0.508037505 -0.55421976
133 0 1 0 1.31788921 -0.376730699 0.04506631
135 1 0 1 -0.38135463 -0.405909990 -0.55421976
137 0 0 0 -0.84478477 -0.147511813 0.64435239
139 0 1 1 -1.07649984 -0.479505995 -0.55421976
140 0 0 1 -0.45859299 0.881869349 -0.55421976
144 0 1 1 -0.84478477 -0.527490365 -0.55421976
145 1 0 1 -0.92202313 -0.435089280 -0.55421976
152 0 0 0 -0.61306970 0.636763311 0.04506631
155 0 1 1 0.00000000 -0.516548131 -0.55421976
159 0 1 1 0.00000000 -0.490286770 -0.55421976
161 0 1 1 1.08617414 -0.345606123 0.04506631
165 0 1 1 -2.23507519 0.113238214 2.44221062
166 0 1 1 -1.61716834 -0.259527217 0.64435239
168 0 1 0 1.16341250 -0.116062374 2.44221062
170 0 1 1 -0.14963956 0.440207722 -0.55421976
172 0 1 1 -2.00336012 -0.092232621 2.44221062
175 0 0 1 2.01303442 -0.061676068 -0.55421976
177 0 1 1 0.00000000 -0.163397019 1.84292454
178 0 0 0 1.54960428 -0.100256925 -0.55421976
183 0 1 1 -1.61716834 -0.048220525 3.04149669
186 0 0 1 0.00000000 0.313845834 -0.55421976
187 0 1 0 0.00000000 -0.357277839 0.04506631
193 0 1 0 -0.84478477 -0.506010517 0.04506631
195 0 0 0 1.08617414 -0.119548327 -0.55421976
201 0 1 1 -0.14963956 -0.473995000 -0.55421976
208 0 1 1 -0.30411628 -0.293326562 -0.55421976
210 0 0 1 0.77722072 -0.055758508 -0.55421976
215 0 1 1 0.00000000 -0.508037505 0.04506631
221 0 1 1 -1.07649984 -0.502201647 -0.55421976
222 1 0 1 -0.22687792 -0.405909990 -0.55421976
225 0 0 1 0.62274400 1.091960238 0.04506631
228 0 1 1 -0.72892724 -0.517763935 -0.55421976
231 0 0 0 0.39102893 0.965030325 0.04506631
234 0 1 0 -1.92612177 -0.048220525 3.04149669
239 1 0 1 -0.84478477 -0.454542140 -0.55421976
241 0 1 0 0.00000000 -0.377621640 0.04506631
250 1 0 1 1.85855771 -0.153022808 0.04506631
251 0 1 1 0.00000000 -0.517763935 -0.55421976
256 0 1 0 -0.07240121 -0.362222756 0.64435239
260 1 0 0 1.54960428 -0.153022808 0.04506631
261 0 1 1 0.00000000 -0.508037505 -0.55421976
263 0 0 1 1.70408100 0.890623136 0.64435239
268 0 1 1 -0.38135463 -0.507551184 0.04506631
271 0 0 1 0.00000000 -0.055758508 -0.55421976
272 0 1 1 -0.38135463 -0.658797171 -0.55421976
274 0 0 1 0.54550565 -0.081047226 0.04506631
277 0 1 0 1.16341250 -0.508037505 -0.55421976
279 0 1 1 -1.77164505 -0.092232621 2.44221062
282 0 1 1 -0.14963956 -0.506010517 -0.55421976
283 0 1 1 -1.07649984 -0.473995000 -0.55421976
284 0 1 1 -0.84478477 -0.502201647 -0.55421976
295 0 1 1 -0.45859299 -0.505201278 -0.55421976
301 0 1 0 0.00000000 -0.508037505 -0.55421976
308 0 0 0 -0.99926149 1.459619293 0.04506631
310 0 0 0 0.00483715 0.448638592 -0.55421976
312 0 0 0 -0.92202313 4.445146996 1.84292454
313 1 0 0 -0.30411628 -0.153022808 0.64435239
314 0 1 1 -0.14963956 -0.505201278 -0.55421976
318 1 0 1 1.85855771 -0.386457129 -0.55421976
321 0 1 1 -0.61306970 -0.517763935 -0.55421976
326 0 0 0 0.46826729 1.979658438 -0.55421976
328 1 0 0 0.46826729 -0.405909990 -0.55421976
333 0 0 1 0.62274400 2.326487371 0.04506631
334 0 1 1 -1.07649984 -0.308645689 0.64435239
338 0 0 0 0.85445907 1.957612512 -0.55421976
344 1 0 1 -0.38135463 -0.405909990 -0.55421976
347 1 0 0 0.77722072 -0.405909990 -0.55421976
351 0 1 1 -0.53583135 -0.479344536 -0.55421976
358 1 0 0 0.62274400 -0.405909990 -0.55421976
359 0 1 0 0.00000000 -0.505524195 -0.55421976
364 0 1 1 0.39102893 -0.521654507 -0.55421976
369 0 1 0 0.00000000 -0.508037505 -0.55421976
375 0 1 0 -2.08059848 -0.248828144 1.84292454
378 0 0 1 -0.22687792 3.455482739 0.64435239
380 0 1 1 -0.84478477 -0.507551184 -0.55421976
400 1 0 0 -0.14963956 -0.412718491 -0.55421976
403 0 1 0 -0.69030806 -0.467672820 0.04506631
409 0 1 1 -0.69030806 -0.507551184 -0.55421976
412 0 1 1 0.00000000 -0.525383620 -0.55421976
413 0 0 0 0.23655222 1.091960238 0.04506631
415 0 1 1 1.08617414 -0.504633254 -0.55421976
416 0 1 0 0.00000000 -0.502201647 -0.55421976
418 1 0 0 -0.92202313 -0.405909990 0.64435239
419 1 0 1 0.00483715 -0.405909990 -0.55421976
420 0 1 0 -1.53992998 -0.189010600 0.64435239
424 0 1 0 -0.14963956 -0.378675985 0.64435239
427 1 0 0 -0.14963956 -0.153022808 0.04506631
430 0 1 1 0.15931386 -0.502201647 -0.55421976
436 0 0 0 -1.23097656 1.675546040 1.24363847
440 1 0 1 0.08207551 -0.454542140 -0.55421976
441 1 0 0 1.16341250 -0.148159593 0.64435239
442 0 1 1 -0.76754642 -0.473995000 -0.55421976
445 0 1 1 0.00000000 -0.500985843 -0.55421976
447 1 0 0 -1.30821491 -0.279466399 0.04506631
448 0 0 1 0.31379058 -0.142323735 -0.55421976
450 0 0 1 1.70408100 -0.065484938 -0.55421976
452 0 1 1 0.00000000 -0.270387749 0.04506631
455 0 1 1 0.00000000 -0.502201647 -0.55421976
457 0 0 1 2.70817963 -0.142323735 -0.55421976
458 0 0 0 0.00000000 0.350076786 0.04506631
462 0 1 1 0.31379058 -0.502201647 -0.55421976
467 1 0 1 0.00000000 -0.658797171 -0.55421976
469 0 1 1 0.00000000 -0.508523827 -0.55421976
473 1 0 0 0.23655222 -0.118980303 1.24363847
476 0 0 1 0.00000000 0.352751554 -0.55421976
477 1 0 1 0.31379058 -0.250287109 0.04506631
478 0 1 1 -0.07240121 -0.521736209 0.04506631
482 1 0 1 0.00000000 -0.658797171 -0.55421976
483 0 1 1 1.54960428 -0.502201647 -0.55421976
488 0 0 1 2.16751113 -0.081047226 -0.55421976
490 0 1 1 -1.61716834 -0.349496695 0.64435239
492 0 1 1 -0.69030806 -0.517763935 -0.55421976
495 0 1 1 -0.69030806 -0.502201647 -0.55421976
496 0 1 1 0.00000000 -0.377541884 -0.55421976
510 0 1 1 -0.30411628 0.440207722 -0.55421976
515 0 1 1 -0.45859299 -0.512982422 -0.55421976
520 0 1 1 0.15931386 -0.505201278 -0.55421976
523 0 1 1 0.00000000 -0.518250257 -0.55421976
525 0 1 1 0.00000000 -0.518168555 -0.55421976
527 1 0 0 1.54960428 -0.454542140 -0.55421976
529 0 1 1 0.69998236 -0.504633254 -0.55421976
531 1 0 0 -2.15783684 -0.153022808 0.64435239
534 0 1 0 0.00000000 -0.223864289 0.64435239
539 0 1 1 0.00000000 -0.376730699 -0.55421976
540 0 0 0 -0.61306970 0.304119404 0.64435239
550 1 0 1 -1.69440670 0.056095438 0.64435239
551 0 0 1 -0.99926149 1.498200151 0.64435239
554 0 1 1 -0.61306970 -0.518250257 -0.55421976
562 0 1 1 0.77722072 -0.505201278 -0.55421976
566 0 1 1 -0.45859299 -0.189010600 0.64435239
571 1 0 1 2.47646456 -0.454542140 -0.55421976
572 0 0 0 1.78131935 0.342620505 0.64435239
574 0 1 0 0.00000000 -0.508037505 -0.55421976
582 0 0 0 0.69998236 1.498200151 0.64435239
583 1 0 1 1.85855771 -0.153022808 -0.55421976
586 0 0 0 -0.92202313 0.890623136 0.64435239
589 0 1 1 -0.61306970 -0.502201647 -0.55421976
599 0 1 1 0.00000000 -0.518250257 -0.55421976
602 0 1 1 0.00000000 -0.505201278 -0.55421976
604 0 1 1 1.08617414 -0.502201647 -0.55421976
607 0 1 1 0.00483715 -0.505201278 -0.55421976
620 1 0 1 -0.30411628 -0.454542140 -0.55421976
628 0 0 0 -0.69030806 0.857714732 -0.55421976
629 0 1 1 -0.30411628 -0.505201278 -0.55421976
630 0 1 1 0.00000000 -0.508362368 -0.55421976
632 0 1 1 1.62684264 -0.521572805 -0.55421976
636 1 0 0 -0.14963956 -0.405909990 -0.55421976
643 0 1 0 -2.15783684 -0.116062374 2.44221062
652 1 0 0 -0.92202313 -0.211381389 0.04506631
653 0 1 1 -0.69030806 -0.494745366 -0.55421976
657 0 1 1 0.00000000 -0.505201278 -0.55421976
660 0 0 1 2.16751113 1.544725556 0.64435239
664 0 1 1 0.46826729 -0.512982422 -0.55421976
665 0 1 1 -0.76754642 -0.504633254 0.04506631
666 1 0 1 0.15931386 0.770988046 0.64435239
669 0 1 1 1.00893579 -0.502201647 -0.55421976
672 0 0 1 0.08207551 0.352751554 0.04506631
673 1 0 1 3.09437141 -0.454542140 -0.55421976
675 1 0 1 0.00000000 -0.658797171 -0.55421976
676 0 1 1 -0.92202313 -0.507551184 -0.55421976
680 0 0 1 0.46826729 9.307471078 0.04506631
684 0 1 1 -1.23097656 0.253541968 3.64078277
685 1 0 1 2.32198785 0.099864373 0.64435239
686 1 0 1 -0.38135463 0.150037190 1.24363847
688 0 1 1 -0.84478477 -0.460946021 -0.55421976
696 1 0 1 1.70408100 -0.396183559 -0.55421976
697 0 1 1 1.08617414 -0.502201647 -0.55421976
698 0 1 0 0.00000000 -0.508362368 -0.55421976
700 0 1 1 0.93169743 -0.509982791 -0.55421976
706 1 0 1 0.69998236 -0.153022808 -0.55421976
707 1 0 0 1.16341250 -0.396183559 -0.55421976
711 0 0 0 -0.45859299 0.304201106 -0.55421976
715 1 0 1 1.70408100 -0.405909990 -0.55421976
722 0 1 1 -0.99926149 -0.521572805 0.04506631
724 1 0 1 1.54960428 -0.405909990 -0.55421976
727 1 0 0 0.00483715 -0.250287109 1.24363847
731 0 0 0 -0.07240121 3.452321649 -0.55421976
733 1 0 1 0.00000000 -0.658797171 -0.55421976
734 1 0 1 -0.53583135 -0.405909990 -0.55421976
741 0 0 1 0.00000000 -0.075211368 -0.55421976
746 0 0 1 3.09437141 0.722355896 0.64435239
747 0 1 1 -1.07649984 -0.264876754 0.64435239
748 1 0 0 0.00483715 -0.405909990 -0.55421976
751 1 0 0 -2.00336012 -0.211381389 0.64435239
756 1 0 1 -2.26056385 -0.376730699 0.64435239
758 1 0 1 -0.92202313 -0.435089280 -0.55421976
761 0 1 1 0.00000000 -0.376730699 -0.55421976
765 0 1 1 -1.07649984 -0.507551184 -0.55421976
772 0 1 1 1.39512757 -0.506010517 -0.55421976
775 1 0 0 1.85855771 -0.211381389 1.84292454
776 0 1 1 -0.92202313 -0.508037505 -0.55421976
779 0 1 1 0.00000000 -0.508280666 -0.55421976
785 0 1 1 -0.38135463 -0.521654507 -0.55421976
787 0 1 0 -0.92202313 -0.512982422 -0.55421976
788 0 1 1 -1.69440670 -0.092232621 2.44221062
798 0 1 0 0.08207551 -0.489882151 -0.55421976
802 1 0 0 0.08207551 -0.148159593 0.64435239
805 0 1 1 -0.22687792 -0.523113472 -0.55421976
807 0 0 1 0.69998236 -0.658797171 -0.55421976
809 1 0 1 0.69998236 -0.405909990 -0.55421976
810 0 0 0 0.23655222 0.374149700 0.04506631
814 0 1 0 -1.84888341 -0.050408971 3.04149669
818 1 0 1 0.08207551 0.061040355 0.64435239
820 0 1 1 -1.53992998 -0.116062374 2.44221062
821 0 0 0 1.70408100 1.160045248 0.64435239
829 0 1 1 0.00000000 -0.508037505 -0.55421976
830 0 0 0 2.47646456 0.897431637 -0.55421976
834 0 1 1 -0.53583135 -0.506010517 -0.55421976
837 0 1 1 -0.69030806 -0.490286770 -0.55421976
839 0 1 1 0.15931386 0.440207722 -0.55421976
841 0 1 1 -0.76754642 -0.504633254 -0.55421976
842 1 0 1 -1.07649984 -0.454542140 -0.55421976
844 0 1 1 0.35240975 -0.533569384 -0.55421976
848 0 1 1 0.39102893 -0.505201278 -0.55421976
854 0 0 0 -1.07649984 0.107645517 0.04506631
863 0 0 0 1.39512757 -0.154400071 -0.55421976
866 1 0 0 0.93169743 -0.405909990 -0.55421976
869 0 1 1 0.00000000 -0.473995000 -0.55421976
870 0 1 1 -2.00336012 -0.442222643 0.64435239
873 0 0 1 0.23655222 -0.561532870 -0.55421976
875 1 0 0 -0.14963956 -0.191928529 0.04506631
876 0 1 0 -1.15373820 -0.518250257 -0.55421976
884 1 0 1 -0.14963956 -0.454542140 -0.55421976
890 0 0 1 -0.30411628 -0.075211368 -0.55421976
891 0 1 1 0.15931386 -0.508037505 -0.55421976
12.6 모형 훈련
Package "glmnet"
에서 제공하는 함수 glmnet()
을 이용하여 Ridge Regression
을 수행할 수 있다. 함수 glmnet()
는 Target이 2개의 클래스를 가질 때 “두 번째 클래스”에 속할 확률을 모델링하며, “두 번째 클래스”란 “Factor” 변환하였을 때 두 번째 수준(Level)을 의미한다. 예를 들어, “a”와 “b” 2개의 클래스를 가진 Target을 “Factor” 변환하였을 때 수준이 “a” “b”라면, 첫 번째 클래스는 “a”, 두 번째 클래스는 “b”가 된다. 함수 glmnet()
에 대한 자세한 옵션은 여기를 참고한다.
glmnet(x, y, family, alpha, lambda, ...)
x
: 예측 변수를 포함하는 행렬y
: Target을 포함하는 변수family
: Target의 분포"gaussian"
: 수치형인 Target"binomial"
: 2개의 클래스를 가지는 Target"multinomial"
: 3개 이상 클래스를 가지는 Target"poisson"
: Count Data인 Target
alpha
: Elasticnet Mixing Parameter0
: Ridge Regression1
: Lasso Regression0 < alpha < 1
: Elastic Net Regression
lambda
: Regularization Parameter- 직접 값을 지정하면 해당 값에 대한 결과만 보여준다.
- 값을 지정하지 않으면 100개의
lambda
값에 대한 결과를 보여준다.
12.6.1 람다 값 직접 지정
<- glmnet(x = train.x, # 예측 변수를 포함하는 행렬
ridge.fit y = titanic.trd.Imp$Survived,# Target
family = "binomial", # Binary Classification
alpha = 0, # 0 : Ridge / 1 : Lasso / 0 < alpha < 1 : Elastic Net
lambda = 1)
round(coef(ridge.fit), 3) # 회귀계수 추정치
7 x 1 sparse Matrix of class "dgCMatrix"
s0
(Intercept) -0.099
Pclass2 0.063
Pclass3 -0.240
Sexmale -0.409
Age -0.036
Fare 0.090
FamSize -0.013
Result!
데이터 “titanic.trd.Imp”의 Target “Survived”은 “no”와 “yes” 2개의 클래스를 가지며, “Factor” 변환하면 알파벳순으로 수준을 부여하기 때문에 “yes”가 두 번째 클래스가 된다. 즉, “yes”에 속할 확률(= 탑승객이 생존할 확률)을 \(p\)라고 할 때, 추정된 회귀계수를 이용하여 다음과 같은 모형식을 얻을 수 있다. \[
\begin{align*}
\log{\frac{p}{1-p}} = &-0.099 + 0.063 X_{\text{Pclass2}} - 0.240 X_{\text{Pclass3}} -0.409 X_{\text{Sexmale}} \\
&-0.036 Z_{\text{Age}} +0.090 Z_{\text{Fare}} - 0.013 Z_{\text{FamSize}}
\end{align*}
\] 여기서, \(Z_{\text{예측 변수}}\)는 표준화한 예측 변수, \(X_{\text{예측 변수}}\)는 더미 변수를 의미한다.
12.6.2 교차 검증을 통한 최적의 람다 값
# 100개의 람다 값에 따른 결과
<- glmnet(x = train.x, # 예측 변수를 포함하는 행렬
ridge.fit y = titanic.trd.Imp$Survived,# Target
family = "binomial", # Binary Classification
alpha = 0) # 0 : Ridge / 1 : Lasso / 0 < alpha < 1 : Elastic Net
plot(ridge.fit, xvar = "lambda") # 람다 값에 따른 회귀계수 추정치 확인
Result!
100개의 \(\lambda\) 값에 대한 회귀계수 추정치의 변화를 보여준다. 해당 그림을 통해 \(\lambda\) 값이 클수록 회귀계수 추정치는 작아진다는 것을 알 수 있다.
$lambda # 100개의 람다 값 ridge.fit
[1] 250.77547111 228.49726943 208.19820178 189.70244735 172.84980477 157.49430451 143.50294457 130.75453848 119.13866566 108.55471496 98.91101327 90.12403146 82.11766090 74.82255423 68.17552473
[16] 62.11899902 56.60051835 51.57228430 46.99074470 42.81621645 39.01254179 35.54677510 32.38889757 29.51155718 26.88983178 24.50101323 22.32441074 20.34117162 18.53411800 16.88759804
[31] 15.38735037 14.02038057 12.77484861 11.63996626 10.60590374 9.66370449 8.80520761 8.02297724 7.31023805 6.66081665 6.06908806 5.52992700 5.03866353 4.59104255 4.18318698
[46] 3.81156418 3.47295532 3.16442754 2.88330852 2.62716334 2.39377339 2.18111716 1.98735272 1.81080178 1.64993514 1.50335945 1.36980514 1.24811543 1.13723629 1.03620736
[61] 0.94415355 0.86027755 0.78385286 0.71421754 0.65076842 0.59295595 0.54027937 0.49228244 0.44854943 0.40870153 0.37239362 0.33931119 0.30916772 0.28170211 0.25667647
[76] 0.23387404 0.21309732 0.19416634 0.17691714 0.16120031 0.14687972 0.13383133 0.12194212 0.11110912 0.10123849 0.09224474 0.08404997 0.07658321 0.06977976 0.06358072
[91] 0.05793239 0.05278583 0.04809648 0.04382373 0.03993055 0.03638323 0.03315104 0.03020599 0.02752258 0.02507755
coef(ridge.fit) # 100개의 람다 값에 따른 회귀계수 추정치
7 x 100 sparse Matrix of class "dgCMatrix"
(Intercept) -4.726044e-01 -4.704569e-01 -4.702478e-01 -4.700184e-01 -4.697668e-01 -4.694907e-01 -4.691878e-01 -4.688555e-01 -4.684910e-01 -4.680912e-01 -4.676526e-01 -4.671715e-01 -4.666440e-01
Pclass2 1.157524e-37 5.000604e-04 5.487603e-04 6.020873e-04 6.605777e-04 7.247275e-04 7.950798e-04 8.722285e-04 9.568236e-04 1.049576e-03 1.151262e-03 1.262731e-03 1.384909e-03
Pclass3 -3.278078e-37 -1.417983e-03 -1.556055e-03 -1.707480e-03 -1.873610e-03 -2.055867e-03 -2.255809e-03 -2.475142e-03 -2.715736e-03 -2.979639e-03 -3.269094e-03 -3.586555e-03 -3.934709e-03
Sexmale -5.262198e-37 -2.277161e-03 -2.498915e-03 -2.742203e-03 -3.009139e-03 -3.302014e-03 -3.623340e-03 -3.975869e-03 -4.362618e-03 -4.786891e-03 -5.252312e-03 -5.762846e-03 -6.322839e-03
Age -3.962343e-38 -1.716261e-04 -1.883474e-04 -2.067040e-04 -2.268490e-04 -2.489563e-04 -2.732169e-04 -2.998405e-04 -3.290567e-04 -3.611178e-04 -3.963005e-04 -4.349081e-04 -4.772734e-04
Fare 1.325639e-37 5.732668e-04 6.290431e-04 6.902341e-04 7.573627e-04 8.310019e-04 9.117794e-04 1.000383e-03 1.097565e-03 1.204150e-03 1.321040e-03 1.449223e-03 1.589777e-03
FamSize -3.833817e-39 -1.690088e-05 -1.857967e-05 -2.042846e-05 -2.246511e-05 -2.470948e-05 -2.718368e-05 -2.991237e-05 -3.292304e-05 -3.624643e-05 -3.991691e-05 -4.397301e-05 -4.845794e-05
(Intercept) -4.660653e-01 -4.654308e-01 -4.647351e-01 -4.639723e-01 -4.631359e-01 -0.4622191570 -4.612143e-01 -0.4601141058 -0.4589075318 -0.4575855856 -0.4561374736 -0.4545514404 -0.4528146926
Pclass2 1.518810e-03 1.665536e-03 1.826294e-03 2.002393e-03 2.195265e-03 0.0024064623 2.637677e-03 0.0028879564 0.0031643067 0.0034665479 0.0037969936 0.0041581416 0.0045526835
Pclass3 -4.316497e-03 -4.735133e-03 -5.194136e-03 -5.697349e-03 -6.248974e-03 -0.0068535974 -7.516230e-03 -0.0082405117 -0.0090356875 -0.0099067224 -0.0108606777 -0.0119052365 -0.0130487520
Sexmale -6.937047e-03 -7.610680e-03 -8.349438e-03 -9.159557e-03 -1.004786e-02 -0.0110218069 -1.208955e-02 -0.0132592013 -0.0145419363 -0.0159477048 -0.0174880927 -0.0191757336 -0.0210243943
Age -5.237615e-04 -5.747729e-04 -6.307465e-04 -6.921638e-04 -7.595528e-04 -0.0008334922 -9.146167e-04 -0.0010038119 -0.0011014984 -0.0012086708 -0.0013262461 -0.0014552286 -0.0015967189
Fare 1.743884e-03 1.912834e-03 2.098037e-03 2.301035e-03 2.523507e-03 0.0027672890 3.034382e-03 0.0033269807 0.0036474327 0.0039983341 0.0043824938 0.0048029629 0.0052630518
FamSize -5.342027e-05 -5.891465e-05 -6.500269e-05 -7.175397e-05 -7.924718e-05 -0.0000875715 -9.682813e-05 -0.0001071327 -0.0001186154 -0.0001314263 -0.0001457363 -0.0001617414 -0.0001796661
(Intercept) -0.4509133186 -0.4488322041 -0.446554945 -0.4440637571 -0.4413393811 -0.4383609899 -0.4351060925 -0.4315504395 -0.4276679311 -0.4234305300 -0.4188081803 -0.4137687371 -0.408277908
Pclass2 0.0049835144 0.0054537409 0.005966689 0.0065259094 0.0071351808 0.0077985105 0.0085201309 0.0093044918 0.0101562467 0.0110802319 0.0120814380 0.0131649707 0.014336000
Pclass3 -0.0143002968 -0.0156697153 -0.017167677 -0.0188057312 -0.0205963633 -0.0225530494 -0.0246903118 -0.0270237712 -0.0295701964 -0.0323475493 -0.0353750227 -0.0386730700 -0.042263424
Sexmale -0.0230490668 -0.0252660640 -0.027693122 -0.0303495034 -0.0332561128 -0.0364356055 -0.0399125064 -0.0437133275 -0.0478666856 -0.0524034179 -0.0573566938 -0.0627621186 -0.068657827
Age -0.0017519219 -0.0019221572 -0.002108869 -0.0023136384 -0.0025381942 -0.0027844272 -0.0030544045 -0.0033503841 -0.0036748320 -0.0040304390 -0.0044201397 -0.0048471319 -0.005314897
Fare 0.0057663482 0.0063167352 0.006918410 0.0075759010 0.0082940872 0.0090782133 0.0099339054 0.0108671847 0.0118844766 0.0129926182 0.0141988591 0.0155108573 0.016936667
FamSize -0.0001997685 -0.0002223454 -0.000247739 -0.0002763438 -0.0003086157 -0.0003450817 -0.0003863519 -0.0004331326 -0.0004862427 -0.0005466314 -0.0006153993 -0.0006938227 -0.000783381
(Intercept) -0.4022992114 -0.395793956 -0.388721246 -0.381038012 -0.372699085 -0.363657302 -0.353863663 -0.343267537 -0.33181692 -0.319458756 -0.306139310 -0.291804612 -0.276400948 -0.259875415
Pclass2 0.0155996978 0.016961155 0.018425287 0.019996714 0.021679625 0.023477608 0.025393469 0.027429008 0.02958478 0.031859817 0.034251342 0.036754436 0.039361704 0.042062924
Pclass3 -0.0461691019 -0.050414393 -0.055024830 -0.060027130 -0.065449124 -0.071319637 -0.077668356 -0.084525653 -0.09192237 -0.099889595 -0.108458341 -0.117659279 -0.127522377 -0.138076555
Sexmale -0.0750845646 -0.082085743 -0.089707480 -0.097998602 -0.107010614 -0.116797624 -0.127416212 -0.138925246 -0.15138563 -0.164859959 -0.179412132 -0.195106828 -0.212008923 -0.230182799
Age -0.0058272212 -0.006388218 -0.007002351 -0.007674454 -0.008409757 -0.009213906 -0.010092982 -0.011053521 -0.01210253 -0.013247488 -0.014496371 -0.015857633 -0.017340205 -0.018953478
Fare 0.0184847193 0.020163788 0.021982949 0.023951522 0.026078995 0.028374937 0.030848886 0.033510220 0.03636801 0.039430838 0.042706633 0.046202441 0.049924225 0.053876637
FamSize -0.0008857883 -0.001003029 -0.001137399 -0.001291548 -0.001468533 -0.001671872 -0.001905604 -0.002174354 -0.00248340 -0.002838749 -0.003247205 -0.003716445 -0.004255088 -0.004872758
(Intercept) -0.242176518 -0.223254813 -0.203063566 -0.181559439 -0.15870316 -0.13446019 -0.10880134 -0.08170331 -0.05314921 -0.02312896 0.008360434 0.04131468 0.07572214 0.11156388 0.14881382
Pclass2 0.044844684 0.047690037 0.050578167 0.053484086 0.05637838 0.05922705 0.06199135 0.06462784 0.06708843 0.06932063 0.071267854 0.07286990 0.07406349 0.07478294 0.07496086
Pclass3 -0.149349317 -0.161366396 -0.174151415 -0.187725583 -0.20210744 -0.21731267 -0.23335402 -0.25024124 -0.26798125 -0.28657828 -0.306034193 -0.32634889 -0.34752074 -0.36954712 -0.39242492
Sexmale -0.249691564 -0.270596175 -0.292954488 -0.316820231 -0.34224192 -0.36926176 -0.39791451 -0.42822638 -0.46021398 -0.49388338 -0.529229220 -0.56623403 -0.60486771 -0.64508724 -0.68683655
Age -0.020707272 -0.022611796 -0.024677597 -0.026915496 -0.02933651 -0.03195174 -0.03477231 -0.03780923 -0.04107326 -0.04457484 -0.048323903 -0.05232981 -0.05660122 -0.06114596 -0.06597100
Fare 0.058062798 0.062484082 0.067139925 0.072027651 0.07714234 0.08247676 0.08802129 0.09376399 0.09969060 0.10578476 0.112028090 0.11840043 0.12488008 0.13144400 0.13806810
FamSize -0.005580138 -0.006389012 -0.007312285 -0.008363985 -0.00955923 -0.01091417 -0.01244590 -0.01417229 -0.01611186 -0.01828353 -0.020706366 -0.02339931 -0.02638086 -0.02966871 -0.03327942
(Intercept) 0.18743910 0.22740041 0.26865250 0.31114477 0.35482181 0.39962413 0.44548870 0.49234964 0.54013871 0.58878582 0.63821946 0.68836691 0.7391545215 0.79051795 0.84236586
Pclass2 0.07452897 0.07341891 0.07156304 0.06889525 0.06535171 0.06087163 0.05539787 0.04887762 0.04126293 0.03251127 0.02258603 0.01145705 -0.0008989611 -0.01451876 -0.02937769
Pclass3 -0.41615111 -0.44072319 -0.46613960 -0.49240001 -0.51950554 -0.54745873 -0.57626343 -0.60592448 -0.63644731 -0.66783729 -0.70009901 -0.73323550 -0.7672472489 -0.80214210 -0.83789634
Sexmale -0.73004678 -0.77463660 -0.82051300 -0.86757212 -0.91570047 -0.96477620 -1.01467065 -1.06524992 -1.11637653 -1.16791112 -1.21971405 -1.27164697 -1.3235742450 -1.37536496 -1.42689128
Age -0.07108228 -0.07648476 -0.08218225 -0.08817745 -0.09447190 -0.10106590 -0.10795856 -0.11514768 -0.12262980 -0.13040007 -0.13845226 -0.14677867 -0.1553700522 -0.16421588 -0.17330330
Fare 0.14472741 0.15139633 0.15804881 0.16465846 0.17119870 0.17764284 0.18396413 0.19013578 0.19613103 0.20192314 0.20748541 0.21279129 0.2178144105 0.22252645 0.22690532
FamSize -0.03722809 -0.04152795 -0.04619008 -0.05122309 -0.05663286 -0.06242225 -0.06859098 -0.07513539 -0.08204839 -0.08931937 -0.09693417 -0.10487509 -0.1131210348 -0.12164724 -0.13042659
(Intercept) 0.89462884 0.94722942 1.00008867 1.0531259 1.1062585 1.1594015 1.2125066 1.2654172 1.3180708 1.3703731 1.4222273 1.4735355 1.5241984 1.5741166 1.6231914 1.6714232
Pclass2 -0.04549014 -0.06284768 -0.08143274 -0.1012183 -0.1221676 -0.1442340 -0.1674321 -0.1915727 -0.2166347 -0.2425331 -0.2691743 -0.2964570 -0.3242730 -0.3525084 -0.3810453 -0.4099249
Pclass3 -0.87450473 -0.91194873 -0.95020275 -0.9892333 -1.0289986 -1.0694476 -1.1105657 -1.1522062 -1.1943246 -1.2368329 -1.2796346 -1.3226253 -1.3656935 -1.4087219 -1.4515887 -1.4942899
Sexmale -1.47803314 -1.52867656 -1.57871476 -1.6280484 -1.6765859 -1.7242434 -1.7709464 -1.8166226 -1.8612118 -1.9046592 -1.9469167 -1.9879424 -2.0277005 -2.0661609 -2.1032991 -2.1391022
Age -0.18261831 -0.19214504 -0.20186588 -0.2117615 -0.2218109 -0.2319914 -0.2422812 -0.2526512 -0.2630766 -0.2735297 -0.2839825 -0.2944063 -0.3047723 -0.3150519 -0.3252167 -0.3352477
Fare 0.23092467 0.23456069 0.23779094 0.2405948 0.2429539 0.2448527 0.2462671 0.2472081 0.2476625 0.2476305 0.2471172 0.2461328 0.2446928 0.2428178 0.2405333 0.2378348
FamSize -0.13942843 -0.14861969 -0.15796506 -0.1674274 -0.1769682 -0.1865482 -0.1961253 -0.2056639 -0.2151237 -0.2244673 -0.2336593 -0.2426665 -0.2514588 -0.2600088 -0.2682928 -0.2762825
(Intercept) 1.7185370
Pclass2 -0.4387237
Pclass3 -1.5364772
Sexmale -2.1735447
Age -0.3451031
Fare 0.2348199
FamSize -0.2839760
coef(ridge.fit)[,100] # 100번째 람다 값에 대한 회귀계수 추정치
(Intercept) Pclass2 Pclass3 Sexmale Age Fare FamSize
1.7185370 -0.4387237 -1.5364772 -2.1735447 -0.3451031 0.2348199 -0.2839760
Caution!
\(\lambda\)는 모형이 Training Dataset
에 과적합 되는 것을 방지하기 위해 사용하는 모수이며, 교차 검증(Cross Validation)을 통해 최적의 값을 찾을 수 있다. 이러한 방법은 package "glmnet"
에서 제공하는 함수 cv.glmnet()
을 통해 수행할 수 있으며, 함수에 대한 자세한 옵션은 여기를 참고한다.
set.seed(200) # Seed 고정 -> 동일한 결과를 출력하기 위해
<- cv.glmnet(x = train.x, # 예측 변수를 포함하는 행렬
cv.ridge.fit y = titanic.trd.Imp$Survived,# Target
family = "binomial", # Binary Classification
alpha = 0, # 0 : Ridge / 1 : Lasso / 0 < alpha < 1 : Elastic Net
nfolds = 5, # 5-Fold Cross Validation
type.measure = "auc") # AUC에 기반하여 최적의 람다 값 찾기
plot(cv.ridge.fit) # Plot
Result!
100개의 \(\lambda\) 값에 대한 AUC의 변화를 보여준다.
Caution!
만약 \(\lambda\) 값에 대해 직접 후보 값을 지정하고 싶으면 함수 cv.glmnet()
의 옵션 lambda = 후보 값
을 이용하면 된다.
$lambda.min # 최적의 람다 값 cv.ridge.fit
[1] 0.03020599
max(cv.ridge.fit$cvm) # 최적의 람다 값에 대한 AUC
[1] 0.849817
round(coef(cv.ridge.fit, s = cv.ridge.fit$lambda.min), 3) # 최적의 람다 값에 대한 회귀계수 추정치
7 x 1 sparse Matrix of class "dgCMatrix"
s1
(Intercept) 1.623
Pclass2 -0.381
Pclass3 -1.452
Sexmale -2.103
Age -0.325
Fare 0.241
FamSize -0.268
Result!
최적의 \(\lambda\) 값에 대해 추정된 회귀계수를 이용하여 다음과 같은 모형식을 얻을 수 있다. \[
\begin{align*}
\log{\frac{p}{1-p}} =&\; 1.623 - 0.381 X_{\text{Pclass2}} - 1.452 X_{\text{Pclass3}} -2.103 X_{\text{Sexmale}} \\
&-0.325 Z_{\text{Age}} +0.241 Z_{\text{Fare}} - 0.268 Z_{\text{FamSize}}
\end{align*}
\]
12.7 모형 평가
Caution!
모형 평가를 위해 Test Dataset
에 대한 예측 class/확률
이 필요하며, 함수 predict()
를 이용하여 생성한다.
# 예측 class 생성
<- predict(cv.ridge.fit,
test.ridge.class newx = test.x, # Test Dataset including Only 예측 변수
s = "lambda.min", # 최적의 람다 값 기반
type = "class") # 예측 class 생성
%>%
test.ridge.class as_tibble
# A tibble: 266 × 1
lambda.min
<chr>
1 yes
2 no
3 no
4 yes
5 no
6 no
7 yes
8 no
9 no
10 yes
# ℹ 256 more rows
12.7.1 ConfusionMatrix
<- caret::confusionMatrix(as.factor(test.ridge.class), titanic.ted.Imp$Survived,
CM positive = "yes") # confusionMatrix(예측 class, 실제 class, positive = "관심 class")
CM
Confusion Matrix and Statistics
Reference
Prediction no yes
no 151 35
yes 13 67
Accuracy : 0.8195
95% CI : (0.768, 0.8638)
No Information Rate : 0.6165
P-Value [Acc > NIR] : 5.675e-13
Kappa : 0.6021
Mcnemar's Test P-Value : 0.002437
Sensitivity : 0.6569
Specificity : 0.9207
Pos Pred Value : 0.8375
Neg Pred Value : 0.8118
Prevalence : 0.3835
Detection Rate : 0.2519
Detection Prevalence : 0.3008
Balanced Accuracy : 0.7888
'Positive' Class : yes
12.7.2 ROC 곡선
# 예측 확률 생성
<- predict(cv.ridge.fit,
test.ridge.prob newx = test.x, # Test Dataset including Only 예측 변수
s = "lambda.min", # 최적의 람다 값 기반
type = "response") # 예측 확률 생성
%>% # "Survived = yes"에 대한 예측 확률
test.ridge.prob as_tibble
# A tibble: 266 × 1
lambda.min
<dbl>
1 0.830
2 0.299
3 0.161
4 0.663
5 0.129
6 0.287
7 0.640
8 0.298
9 0.129
10 0.835
# ℹ 256 more rows
<- titanic.ted.Imp$Survived # Test Dataset의 실제 class
ac <- as.numeric(test.ridge.prob) # 예측 확률을 수치형으로 변환 pp
12.7.2.1 Package “pROC”
::p_load("pROC")
pacman
<- roc(ac, pp, plot = T, col = "gray") # roc(실제 class, 예측 확률)
ridge.roc <- round(auc(ridge.roc), 3)
auc legend("bottomright", legend = auc, bty = "n")
Caution!
Package "pROC"
를 통해 출력한 ROC 곡선은 다양한 함수를 이용해서 그래프를 수정할 수 있다.
# 함수 plot.roc() 이용
plot.roc(ridge.roc,
col="gray", # Line Color
print.auc = TRUE, # AUC 출력 여부
print.auc.col = "red", # AUC 글씨 색깔
print.thres = TRUE, # Cutoff Value 출력 여부
print.thres.pch = 19, # Cutoff Value를 표시하는 도형 모양
print.thres.col = "red", # Cutoff Value를 표시하는 도형의 색깔
auc.polygon = TRUE, # 곡선 아래 면적에 대한 여부
auc.polygon.col = "gray90") # 곡선 아래 면적의 색깔
# 함수 ggroc() 이용
ggroc(ridge.roc) +
annotate(geom = "text", x = 0.9, y = 1.0,
label = paste("AUC = ", auc),
size = 5,
color="red") +
theme_bw()
12.7.2.2 Package “Epi”
::p_load("Epi")
pacman# install_version("etm", version = "1.1", repos = "http://cran.us.r-project.org")
ROC(pp, ac, plot = "ROC") # ROC(예측 확률, 실제 class)
12.7.2.3 Package “ROCR”
::p_load("ROCR")
pacman
<- prediction(pp, ac) # prediction(예측 확률, 실제 class)
ridge.pred
<- performance(ridge.pred, "tpr", "fpr") # performance(, "민감도", "1-특이도")
ridge.perf plot(ridge.perf, col = "gray") # ROC Curve
<- performance(ridge.pred, "auc") # AUC
perf.auc <- attributes(perf.auc)$y.values
auc legend("bottomright", legend = auc, bty = "n")
12.7.3 향상 차트
12.7.3.1 Package “ROCR”
<- performance(ridge.pred, "lift", "rpp") # Lift Chart
ridge.perf plot(ridge.perf, main = "lift curve",
colorize = T, # Coloring according to cutoff
lwd = 2)