1.5 로지스틱 회귀분석
- 목표 변수가 이분형이거나 범주형인 경우, 로지스틱 회귀분석 수행
- 독립 변수가 하나이고 X이며, 목표 변수 Y가 이분형으로 값을 0 또는 1을 가지는 경우의 승산비
1.5.1 데이터 준비
autoparts <- read.csv("autoparts.csv", header = TRUE)
autoparts1 <- autoparts[autoparts$prod_no == "90784-76001", c(2:11)]
autoparts2 <- autoparts1[autoparts1$c_thickness < 1000, ]
autoparts2$y_faulty <- ifelse((autoparts2$c_thickness < 20) | (autoparts2$c_thickness > 32), 1, 0)
autoparts2$y_faulty <- as.factor(autoparts2$y_faulty)1.5.2 빈도표 작성
table(autoparts2$y_faulty) # 20과 32 사이에 있는 것이 1, 따라서 전체의 13%가 불량##
## 0 1
## 18925 28421.5.3 로지스틱 회귀모형 생성
- family = binomial(logit)을 통해 로지스틱 옵션
m <- glm(y_faulty ~ fix_time + a_speed + b_speed + separation + s_separation +
rate_terms + mpa + load_time + highpressure_time, data = autoparts2,
family = binomial(logit))
summary(m)##
## Call:
## glm(formula = y_faulty ~ fix_time + a_speed + b_speed + separation +
## s_separation + rate_terms + mpa + load_time + highpressure_time,
## family = binomial(logit), data = autoparts2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.3641 -0.3738 -0.2150 -0.1184 5.1771
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.511e+02 9.981e+00 -45.197 < 2e-16 ***
## fix_time -3.034e-02 9.623e-03 -3.153 0.001617 **
## a_speed 1.965e+01 9.472e-01 20.743 < 2e-16 ***
## b_speed -1.854e+00 3.970e-01 -4.670 3.01e-06 ***
## separation 5.322e-01 1.121e-02 47.476 < 2e-16 ***
## s_separation 4.957e-01 1.094e-02 45.320 < 2e-16 ***
## rate_terms -2.332e-02 6.817e-03 -3.422 0.000623 ***
## mpa -1.416e-01 3.367e-03 -42.043 < 2e-16 ***
## load_time 1.835e-03 1.508e-02 0.122 0.903124
## highpressure_time 1.787e-04 1.863e-05 9.595 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 16867.6 on 21766 degrees of freedom
## Residual deviance: 9993.8 on 21757 degrees of freedom
## AIC: 10014
##
## Number of Fisher Scoring iterations: 61.5.4 데이터셋 나누기
t_index <- sample(1:nrow(autoparts2), size = nrow(autoparts2) * 0.7)
train <- autoparts2[t_index, ] # 훈련데이터 (70%) index라는 것은 행 번호를
test <- autoparts2[-t_index, ] # 검증데이터 (30%)
nrow(train); nrow(test)## [1] 15236## [1] 6531head(train)## fix_time a_speed b_speed separation s_separation rate_terms mpa
## 23613 80.7 0.666 1.692 188.9 713.8 84 76.3
## 7863 85.5 0.605 1.666 254.0 650.1 81 77.6
## 21870 81.2 0.645 1.678 185.2 713.1 85 75.9
## 8874 85.8 0.602 1.651 243.4 651.7 81 80.9
## 2797 111.6 0.474 1.597 270.9 633.4 82 29.5
## 15501 81.8 0.651 1.622 186.5 713.4 87 75.4
## load_time highpressure_time c_thickness y_faulty
## 23613 19.2 73 20.7 0
## 7863 18.0 60 20.3 0
## 21870 19.2 68 25.1 0
## 8874 18.1 55 29.3 0
## 2797 18.8 69 31.5 0
## 15501 19.2 68 23.7 01.5.4 모델 만들기 - train set 만 이용
m <- glm(y_faulty ~ fix_time + a_speed + b_speed + separation + s_separation + rate_terms + mpa + load_time + highpressure_time, data = train, family = binomial(logit)) - 훈련데이터에 모델을 적합시킨(훈련데이터를 예측한) 결과를 확인해보면 다음과 같다.
head(m$fitted.values)## 23613 7863 21870 8874 2797 15501
## 0.115132593 0.426804315 0.007933978 0.002858798 0.983451135 0.0234899651.5.6 기준값 설정
- 확률이 50%가 넘는 것만을 선택하도록 해준다.
- 값이 0.5가 넘으면 1(불량)을, 0.5보다 작으면 0(정상)을 가지도록 한다.
yhat <- ifelse(m$fitted.values >= 0.5, 1, 0)
head(yhat)## 23613 7863 21870 8874 2797 15501
## 0 0 0 0 1 01.5.7 빈도표 작성
table <- table(real = train$y_faulty, predict = yhat)
table## predict
## real 0 1
## 0 13043 222
## 1 1046 9251.5.8 예측값 구하기
yhat_test <- predict(m, test, type = "response") # 원래는 이름을 입력해야하지만, test와 train의 변수명이 같으므로
head(yhat_test, n = 20) ## 7 11 12 13 19 20
## 0.03464147 0.39924532 0.06846813 0.23836566 0.01789040 0.03775477
## 21 27 38 39 44 48
## 0.72248631 0.07586966 0.10594576 0.07678122 0.31616370 0.03322495
## 51 52 57 60 66 70
## 0.19023789 0.15502251 0.03292441 0.32996299 0.06214658 0.17382086
## 71 72
## 0.01284900 0.102229561.5.9 ROC, AUC
library(Epi)
ROC(test = yhat_test, stat = test$y_faulty, plot = "ROC", AUC = T, main = "Logistics Regression")
1.5.10 데이터 예측(1) - 1개의 데이터만 예측
new.data <- data.frame(fix_time = 87, a_speed = 0.609, b_speed = 1.715,
separation = 242.7, s_separation = 657.5, rate_terms = 95,
mpa = 78, load_time = 18.1, highpressure_time = 82)
possiblityOf1 <- predict(m, newdata = new.data, type = "response")
ifelse(possiblityOf1 >= 0.5, 1, 0)## 1
## 01.5.11 데이터예측(2) - 복수의 데이터 예측
new.data <- data.frame(fix_time = c(87, 85.7) , a_speed = c(0.609, 0472),
b_speed = c(1.715, 1.685), separation = c(242.7, 243.4),
s_separation = c(657.5, 657.9), rate_terms = c(95, 95),
mpa = c(78, 28.2), load_time = c(18.1,18.2), highpressure_time = c(82,60))
possiblityOf1 <- predict(m, newdata = new.data, type = "response")
ifelse(possiblityOf1 >= 0.5, 1, 0)## 1 2
## 0 11.5.12 데이터예측(3) - 데이터프레
new.data <- data.frame(fix_time = test$fix_time, a_speed = test$a_speed, b_speed = test$b_speed,
separation = test$separation, s_separation = test$s_separation,
rate_terms = test$rate_terms, mpa = test$mpa, load_time = test$load_time,
highpressure_time = test$highpressure_time)
possiblityOf1 <- predict(m, newdata = new.data, type = "response")
head(ifelse(possiblityOf1 >= 0.5, 1, 0))## 1 2 3 4 5 6
## 0 0 0 0 0 01.6 다항 로지스틱 회귀분석
- 이항 로지스틱 회귀모형에서는 값이 0, 1로만 나왔다.
- 그런데 결과 즉 종속변수가 둘 이상일 때는 분포가정을 이항분포가 아니라 다항분포로 가정해야한다.
- 이때 사용되는 모형을 다항 로지스틱 회귀모형이라고 한다. 정
1.6.1 데이터 준비
autoparts2$g_class <- as.factor(ifelse(autoparts2$c_thickness < 20, 1,
ifelse(autoparts2$c_thickness < 32, 2, 3)))
table(autoparts2$g_class)##
## 1 2 3
## 2141 18914 7121.6.2 데이터셋 나누기
t_index <- sample(1:nrow(autoparts2), size = nrow(autoparts2) * 0.7)
train <- autoparts2[t_index, ] # 훈련데이터 (70%) index라는 것은 행 번호를
test <- autoparts2[-t_index, ] # 검증데이터 (30%)1.6.3 모델만들기 - train set만 이용
library(nnet)## Warning: package 'nnet' was built under R version 3.5.1m <- multinom(g_class ~ fix_time + a_speed + b_speed + separation + s_separation + rate_terms + mpa + load_time + highpressure_time, data = train)## # weights: 33 (20 variable)
## initial value 16738.456830
## iter 10 value 5171.522255
## iter 20 value 4835.268453
## iter 30 value 4090.394789
## iter 40 value 3270.683830
## iter 50 value 3210.969667
## iter 60 value 3140.265492
## iter 70 value 2613.339546
## iter 80 value 1909.685586
## iter 90 value 1880.440998
## iter 100 value 1878.428702
## final value 1878.428702
## stopped after 100 iterationssummary(m)## Call:
## multinom(formula = g_class ~ fix_time + a_speed + b_speed + separation +
## s_separation + rate_terms + mpa + load_time + highpressure_time,
## data = train)
##
## Coefficients:
## (Intercept) fix_time a_speed b_speed separation s_separation
## 2 1990.403 0.1420046 -19.50741 4.222045 -2.137699 -2.150391
## 3 2098.059 0.1386844 -31.87339 4.678588 -2.237457 -2.258777
## rate_terms mpa load_time highpressure_time
## 2 0.09981973 -0.7561061 -0.3381408 0.0003838150
## 3 0.07868671 -0.8793112 -0.3618599 0.0004315865
##
## Std. Errors:
## (Intercept) fix_time a_speed b_speed separation
## 2 1.373478e-05 0.02932274 0.0024781562 7.607909e-04 0.004697192
## 3 2.185355e-05 0.03102138 0.0002687239 7.182199e-05 0.005226313
## s_separation rate_terms mpa load_time highpressure_time
## 2 0.003599761 0.01493239 0.01508395 0.09279170 0.002376522
## 3 0.004050097 0.02264706 0.01606554 0.09403988 0.002376670
##
## Residual Deviance: 3756.857
## AIC: 3796.8571.6.4 적합 결과 확인
head(m$fitted.values)## 1 2 3
## 7848 2.245185e-03 0.9964322 1.322643e-03
## 15999 1.093702e-05 0.9993257 6.633417e-04
## 14807 8.471122e-01 0.1528219 6.590417e-05
## 7310 3.214153e-05 0.9989796 9.882730e-04
## 14644 2.176470e-01 0.7820089 3.440587e-04
## 8243 6.584333e-15 0.3824184 6.175816e-011.6.5 검증데이터로 예측값 및 정확도 구하기
yhat_test <- predict(m, test)
table <- table(real = test$g_class, predict = yhat_test)
table## predict
## real 1 2 3
## 1 477 120 3
## 2 79 5566 72
## 3 2 57 155(table[1, 1] + table[2, 2] + table[3, 3])/ sum(table)## [1] 0.9490124연습문제 1-1
로지스틱 모형으로 예측값 구하기기 - 우선 LASSO 를 이용하여 최적의 변수 선택하기
library("glmnet")
yvec <- occu.train$Occupancy
xmat <- as.matrix(occu.train[2:5])
fit.lasso=glmnet(x = xmat, y = yvec, alpha = 1, nlambda = 100) # 람다 100개 생성
fit.lasso.cv = cv.glmnet(x = xmat, y = yvec, nfolds = 4, alpha = 1, lambda = fit.lasso$lambda)
plot(fit.lasso.cv)
fit.lasso.param=fit.lasso.cv$lambda.min # 최적의 람다를 다른 이름으로 저장
fit.lasso.tune=glmnet(x=xmat,y=yvec,alpha=1,lambda = fit.lasso.param) # 최적 람다를 이용한 최종 LASSO 모델
coef(fit.lasso.tune) # 생성 모델의 설명변수 계수 출력 / 계수가 클수록 영향 큼 - train 데이터를 이용하여 로지스틱 모형을 적합하시오.
occu.train <- read.csv("occupancy_train.csv")
m <- glm(Occupancy ~ Light + CO2, data = occu.train,
family = binomial(logit))
summary(m)##
## Call:
## glm(formula = Occupancy ~ Light + CO2, family = binomial(logit),
## data = occu.train)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -6.4938 -0.0371 -0.0343 -0.0334 2.6900
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -9.2601315 0.3540292 -26.16 <2e-16 ***
## Light 0.0184191 0.0007278 25.31 <2e-16 ***
## CO2 0.0040918 0.0003222 12.70 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 8420.3 on 8142 degrees of freedom
## Residual deviance: 1093.7 on 8140 degrees of freedom
## AIC: 1099.7
##
## Number of Fisher Scoring iterations: 9- test 데이터의 예측값 및 정확도
head(m$fitted.values)## 1 2 3 4 5 6
## 0.8231171 0.8281271 0.8184526 0.8152388 0.8129164 0.7901480yhat <- ifelse(m$fitted.values >= 0.5, 1, 0)
head(yhat)## 1 2 3 4 5 6
## 1 1 1 1 1 1#빈도표
table <- table(real = occu.train$Occupancy, predict = yhat)
table## predict
## real 0 1
## 0 6325 89
## 1 6 1723(table[1,1] + table[2,2]) / sum(table) # 정확도## [1] 0.9883335#예측값 구하기
test <- read.csv("Occupancy_test.csv")
yhat_test <- predict(m, test, type = "response")
head(yhat_test, n = 20) ## 1 2 3 4 5 6 7
## 0.9898922 0.9890659 0.9883091 0.9527731 0.9491625 0.9884186 0.9798441
## 8 9 10 11 12 13 14
## 0.9669571 0.9423446 0.9690478 0.9500028 0.9519298 0.9477434 0.9446614
## 15 16 17 18 19 20
## 0.9412643 0.9431798 0.9382741 0.9397963 0.9452468 0.9512463- ROC, AUC
library(Epi)
ROC(test = yhat_test, stat = test$Occupancy, plot = "ROC", AUC = T, main = "Logistics Regression")
연습문제1-2
SVM 모형으로 예측값 구하기
- train 데이터를 이용하여 SVM을 적합
m <- svm(factor(Occupancy) ~ Light + CO2, data = occu.train, cost = 10) - test 데이터의 예측값과 정확도를 구하시오
test <- read.csv("Occupancy_test.csv")
yhat_test <- predict(m, test) # 예측값
table <- table(real = as.factor(test$Occupancy), predict = yhat_test)
table## predict
## real -7.51088313122295 -7.51047395032725 -7.50781427450523
## 0 1 1 1
## 1 0 0 0
## predict
## real -7.50392705599613 -7.50338148146853 -7.50269951330904
## 0 1 1 1
## 1 0 0 0
## predict
## real -7.50229033241334 -7.5016765610698 -7.50147197062195
## 0 1 1 1
## 1 0 0 0
## predict
## real -7.50065360883056 -7.49928967251158 -7.49758475211285 -7.497380161665
## 0 3 1 1 1
## 1 0 0 0 0
## predict
## real -7.49656179987361 -7.49587983171411 -7.49574343808222
## 0 3 1 1
## 1 0 0 0
## predict
## real -7.49553884763437 -7.49519786355463 -7.49492507629083
## 0 1 1 2
## 1 0 0 0
## predict
## real -7.49451589539513 -7.49383392723564 -7.49349294315589
## 0 4 1 5
## 1 0 0 0
## predict
## real -7.49328835270805 -7.49246999091666 -7.49144703867742
## 0 1 4 3
## 1 0 0 0
## predict
## real -7.49110605459767 -7.49083326733387 -7.49042408643818
## 0 1 1 2
## 1 0 0 0
## predict
## real -7.49001490554248 -7.48974211827868 -7.48940113419894
## 0 2 1 3
## 1 0 0 0
## predict
## real -7.4883781819597 -7.48735522972046 -7.48701424564072
## 0 5 3 4
## 1 0 0 0
## predict
## real -7.48633227748122 -7.48592309658553 -7.48565030932173
## 0 6 1 3
## 1 0 0 0
## predict
## real -7.48530932524199 -7.48510473479414 -7.48428637300275
## 0 3 4 8
## 1 0 0 0
## predict
## real -7.48346801121136 -7.48326342076351 -7.48292243668376
## 0 2 7 3
## 1 0 0 0
## predict
## real -7.48264964941997 -7.48224046852427 -7.48183128762858
## 0 1 6 1
## 1 0 0 0
## predict
## real -7.48155850036478 -7.48121751628503 -7.48101292583719
## 0 4 4 1
## 1 0 0 0
## predict
## real -7.48019456404579 -7.4793762022544 -7.47917161180656
## 0 10 3 5
## 1 0 0 0
## predict
## real -7.47883062772681 -7.47855784046301 -7.47814865956732
## 0 2 3 3
## 1 0 0 0
## predict
## real -7.47773947867162 -7.47746669140782 -7.47712570732808
## 0 2 3 7
## 1 0 0 0
## predict
## real -7.47692111688023 -7.47610275508884 -7.4750798028496
## 0 3 11 7
## 1 0 0 0
## predict
## real -7.47473881876986 -7.47446603150606 -7.47405685061036
## 0 2 1 9
## 1 0 0 0
## predict
## real -7.47364766971467 -7.47337488245087 -7.47303389837113
## 0 1 4 2
## 1 0 0 0
## predict
## real -7.47282930792328 -7.47201094613189 -7.47098799389265
## 0 1 6 4
## 1 0 0 0
## predict
## real -7.4706470098129 -7.46996504165341 -7.46955586075771
## 0 4 6 1
## 1 0 0 0
## predict
## real -7.46928307349392 -7.46894208941417 -7.46791913717493
## 0 3 3 9
## 1 0 0 0
## predict
## real -7.46710077538354 -7.4668961849357 -7.46655520085595
## 0 2 6 2
## 1 0 0 0
## predict
## real -7.46587323269646 -7.46519126453696 -7.46485028045722
## 0 5 2 2
## 1 0 0 0
## predict
## real -7.46382732821798 -7.46300896642659 -7.46280437597874 -7.462463391899
## 0 7 1 3 3
## 1 0 0 0 0
## predict
## real -7.4617814237395 -7.46137224284381 -7.46109945558001
## 0 3 1 3
## 1 0 0 0
## predict
## real -7.46075847150026 -7.46055388105242 -7.45973551926103
## 0 5 1 1
## 1 0 0 0
## predict
## real -7.45891715746964 -7.45871256702179 -7.45768961478255
## 0 1 2 3
## 1 0 0 0
## predict
## real -7.45728043388685 -7.45700764662306 -7.45666666254331
## 0 1 3 2
## 1 0 0 0
## predict
## real -7.45564371030407 -7.45496174214458 -7.45482534851268
## 0 1 1 2
## 1 0 0 0
## predict
## real -7.45462075806483 -7.45427977398509 -7.4535978058256 -7.4529158376661
## 0 3 3 5 2
## 1 0 0 0 0
## predict
## real -7.45257485358636 -7.45155190134712 -7.45052894910788
## 0 1 3 1
## 1 0 0 0
## predict
## real -7.45018796502814 -7.44950599686864 -7.4484830446294
## 0 1 2 3
## 1 0 0 0
## predict
## real -7.44746009239017 -7.44664173059877 -7.44643714015093
## 0 6 1 2
## 1 0 0 0
## predict
## real -7.44609615607118 -7.44582336880738 -7.44541418791169
## 0 2 1 3
## 1 0 0 0
## predict
## real -7.44473221975219 -7.44439123567245 -7.44234533119397
## 0 2 2 1
## 1 0 0 0
## predict
## real -7.44200434711423 -7.44173155985043 -7.44132237895473
## 0 2 1 2
## 1 0 0 0
## predict
## real -7.4402994267155 -7.43927647447626 -7.43825352223702
## 0 2 5 2
## 1 0 0 0
## predict
## real -7.43791253815728 -7.43723056999778 -7.43682138910209
## 0 2 1 1
## 1 0 0 0
## predict
## real -7.43654860183829 -7.43620761775854 -7.4351846655193
## 0 2 4 5
## 1 0 0 0
## predict
## real -7.43416171328007 -7.43382072920032 -7.43313876104083
## 0 3 3 4
## 1 0 0 0
## predict
## real -7.43272958014513 -7.43245679288133 -7.43211580880159
## 0 1 1 2
## 1 0 0 0
## predict
## real -7.43109285656235 -7.42972892024337 -7.42863777118818
## 0 5 1 1
## 1 0 0 0
## predict
## real -7.42836498392438 -7.42802399984464 -7.42781940939679
## 0 2 2 1
## 1 0 0 0
## predict
## real -7.4270010476054 -7.42618268581401 -7.42597809536616
## 0 3 1 1
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## 0 1 0 1 1
## 1 0 1 0 0
## predict
## real 1.86710766236455 1.86976733818657 1.89050388781866 1.89433717225959
## 0 1 1 1 1
## 1 0 0 0 0
## predict
## real 1.8947095787634 1.91814850996292 1.93053070119733 1.94914843195147
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 1.95405860269982 1.9554225390188 1.9556953262826 1.96633402957068
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 1.9671974696489 1.96837993404915 1.96843331623093 1.96906661979217
## 0 0 1 0 1
## 1 1 0 1 0
## predict
## real 1.97349469524535 1.97588158380357 1.98136339953574 1.98925703613544
## 0 0 1 0 0
## 1 1 0 1 1
## predict
## real 1.99770456490732 1.999984689102 2.0122393562355 2.01982165864828
## 0 0 1 1 0
## 1 1 0 0 1
## predict
## real 2.02521754537734 2.02776454610675 2.03372827317197 2.03375516197463
## 0 0 0 1 0
## 1 1 1 0 1
## predict
## real 2.03378363247158 2.03540374826447 2.03544706776972 2.03582953695005
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.03869380321992 2.04114888859409 2.04204831221707 2.04682759776417
## 0 0 0 0 1
## 1 1 1 1 0
## predict
## real 2.05135587184306 2.05628858173482 2.06242629517025 2.06344924740949
## 0 0 0 0 0
## 1 2 2 1 1
## predict
## real 2.06979155129277 2.07674762651959 2.07852551631639 2.07981648323731
## 0 0 0 1 0
## 1 1 1 0 1
## predict
## real 2.08083943547654 2.0863920595268 2.09106895786893 2.09431393453755
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.10468064140896 2.1049478927428 2.10794025562445 2.10877245036591
## 0 1 1 0 0
## 1 0 0 1 1
## predict
## real 2.10960665659936 2.11267551331707 2.11459685937141 2.1197116205676
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.12392798794869 2.12491535206675 2.12625376981591 2.12994114295999
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.13088406317552 2.13623900294074 2.13710180863466 2.14017066535237
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.15327634422099 2.15721986933968 2.15736815317794 2.17426907332698
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.18874755117075 2.19096249922484 2.20099434420701 2.2013462024349
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.21109535393957 2.2128002743383 2.29154387134553 2.29669718633967
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.29863399516214 2.29978680279475 2.30259771438214 2.30662427845026
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.3120029161303 2.31573598132104 2.31819994310104 2.33555750872367
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.34228230241176 2.35135308581801 2.35488391247784 2.35851910346051
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.35870885177488 2.36198704402327 2.36362376760605 2.38935589947732
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.40452050430287 2.40711971469301 2.4112826655182 2.4180578757113
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.4219975015929 2.43036891132782 2.4390467055802 2.44506678050396
## 0 1 0 0 0
## 1 0 1 1 1
## predict
## real 2.46872220610734 2.47620899310381 2.50210010587417 2.51355717095364
## 0 1 0 0 0
## 1 0 1 1 1
## predict
## real 2.52894886412151 2.5453214381675 2.56943108170446 2.5774071572434
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.58231142413752 2.58314457306379 2.58558012622026 2.60781998522964
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.60923726935628 2.61198589485678 2.62031183775611 2.64269225609452
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.67120743594124 2.67287087123939 2.68927959216779 2.6913314005005
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.70825607356296 2.7155264555283 2.72133802701746 2.7237634349995
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.74520098124615 2.74792885388412 2.76497805787143 2.76865474082951
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.76905507281868 2.77417574474393 2.7846958209614 2.78884694345366
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.79388760397967 2.79697124783226 2.79935515696407 2.80936530177373
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.82669617630548 2.83735565308057 2.84861107963931 2.84945313934552
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.85146938705633 2.85834838218657 2.86045360020035 2.87140658272764
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.8733516816954 2.87954870866613 2.88909626289902 2.89421102409522
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.89747561547943 2.89791739819507 2.91382923500068 2.92694613013515
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.94449879603998 2.96415721122053 2.96423726386871 2.9709917555734
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.97214809451881 2.97733105253096 2.98093362794404 2.98270378635678
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.98551467044487 2.98578745770868 2.98582784591127 2.98890079957787
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 2.99703996671442 2.99779009731573 2.99780789137632 3.00441416111618
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.01828473022168 3.02124980200225 3.0362531015111 3.04259841232009
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.06346070664702 3.08126300003759 3.08146466605762 3.08339787481902
## 0 0 0 0 0
## 1 1 2 1 1
## predict
## real 3.08637776123378 3.09149252242997 3.09353842690845 3.09435678869984
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.10008532123957 3.10717779009828 3.10888271049702 3.11278772306672
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.11522802130602 3.11577654776073 3.11862299934553 3.12025381219925
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.12646270187705 3.14474535240567 3.15996808449583 3.17072541485451
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.18020769669928 3.1810280631078 3.18783983623135 3.2097790993636
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.23939957414126 3.24748534534639 3.25128066912472 3.25233327813162
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.2778675417557 3.28561627624195 3.3065200900436 3.34157034276334
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.34690446091778 3.35021936568463 3.37473467255412 3.37634775322068
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.38706640603457 3.39763691250668 3.39852647803975 3.40061390199294
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.40364123923594 3.41591365918109 3.42000847506376 3.421031427303
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.42491563888639 3.42559760704589 3.43800942754864 3.44046451292281
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.44386853236394 3.45088739744029 3.45121296687568 3.45192157800228
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.47134644222505 3.47259889659697 3.48553673191032 3.50497282445585
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.51827120356595 3.52270399660264 3.52618791057099 3.52943470789462
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.53232562400642 3.5343715284849 3.53675841704311 3.53918685945018
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.54327866840713 3.55912697267447 3.56279186854856 3.57633333003103
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.57835847477215 3.5836215198066 3.59271302170126 3.59571215490735
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.60700113747082 3.62029951658092 3.62189181001081 3.62982637982471
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.6310361284512 3.6497042344614 3.65614436442898 3.66142219659831
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.66443474670643 3.66665266043729 3.66769630366562 3.66960581451222
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.68425574284713 3.69144068582233 3.69464890872353 3.70453447448458
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.70567550190802 3.71165860472809 3.71270620219211 3.72683483329998
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.72701277390589 3.727840094601 3.7300223170883 3.7305856581772
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.73127948904372 3.73140411621613 3.73606512886724 3.74000568830917
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.74884463829029 3.7566190753085 3.75676340491106 3.7700311386946
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.77704846332562 3.78637366054937 3.78654859422956 3.78656045693662
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.79148842174556 3.79997109041756 3.80017610158717 3.80275367889491
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.8027829677383 3.80367487831347 3.81504301433893 3.82004211414128
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.82204649914504 3.82920419914295 3.83854422475053 3.83947820668681
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.84792572858387 3.8484593854058 3.85323319689637 3.85654223905796
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.85707401500959 3.85868890779819 3.8626562408289 3.8637473898841
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.87049787235447 3.87259275435689 3.875056143626 3.8761888671545
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.8763638008347 3.88389418754609 3.8844881052133 3.89391532171777
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.89696812253201 3.89746032862749 3.90267775723835 3.90548086869343
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.91294397152472 3.91613437974086 3.91728189040423 3.93977201128365
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.94079496352289 3.94279638284989 3.94333010154525 3.96329101575584
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 3.97225736609101 3.97698376112745 3.97862936799089 3.99704250829718
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.00379399307615 4.00877296752545 4.02060995215654 4.02132948555506
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.02922246714598 4.03331427610293 4.0456134833865 4.05030511210292
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.05062156271705 4.0605930024826 4.0612749706421 4.06145884260154
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.06679602955513 4.07782607842936 4.0803700791079 4.09623921873565
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.10113162292272 4.1018876986272 4.10287502837115 4.10833077364707
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.11284173158684 4.12049951865228 4.13386606707903 4.13386609457835
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.14034973488811 4.14142112651452 4.14676417607337 4.16250875727702
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.16375406652539 4.16585335318564 4.1679941593206 4.17448168511783
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.17695224700977 4.17713548458491 4.18095155435096 4.180960382633
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.18216720683168 4.1852567820378 4.18728196802787 4.190661232247
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.19118990502538 4.19197274056901 4.19323580950386 4.1942587617431
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.19937352293929 4.20069897420858 4.20175158321547 4.20370851739099
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.20789812493293 4.20978691729111 4.21198993388988 4.21329469417008
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.2157407587671 4.22892889719944 4.2294483187729 4.23196280018027
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.23236604972244 4.23435848952124 4.23507016944699 4.23834361661255
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.24373533076966 4.24745227818349 4.24775477721354 4.25256703937969
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.25711848698629 4.25826892207748 4.25836376873538 4.26279656177207
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.26381951401131 4.26791132296826 4.26995722744674 4.27098017968598
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.28125431097669 4.28328536644686 4.28734741551379 4.29413454341253
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.29623379569867 4.29655398566694 4.30458352588555 4.30637438216221
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.30858757830956 4.31380192437164 4.31619466866031 4.32170975497086
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.32541017709271 4.32868366550722 4.33389925971343 4.33474420259849
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.33870855620281 4.34346750493544 4.35060345663706 4.35999964614103
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.36960171382781 4.37941311704525 4.38084525018018 4.38166361197157
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.38982651139707 4.39029212264921 4.39100723764022 4.39165003136711
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.39202952799584 4.39475740063383 4.39985508465163 4.4011383820619
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.4113183940103 4.41317054094012 4.41635516217405 4.4263019223407
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.42668244877731 4.4284763119797 4.43075601054202 4.43486606669122
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.43567549020336 4.4366984424426 4.43718587073927 4.4383203788905
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.44608888455519 4.44670969955804 4.44798057384187 4.44936230422144
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.45021035027979 4.45306567827041 4.45345411317839 4.4572227321162
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.45821009623426 4.4616377310923 4.46779323858832 4.46888438764352
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.46939440154923 4.47050331716571 4.47203329700942 4.47499718939412
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.4754283781713 4.47680710849999 4.47868693507962 4.47870472914021
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.4831168311878 4.48482464851505 4.4896162196921 4.48995720377183
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.49331669681253 4.49468063313151 4.49506306793773 4.49507196496802
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.49621354427787 4.49670584662089 4.4971178694465 4.49778204354541
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.49809633653426 4.49834541213359 4.49916377392498 4.49967969855974
## 0 0 0 0 0
## 1 1 3 1 1
## predict
## real 4.5048711185878 4.50693465213107 4.50732959777829 4.50837034407812
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.50945260985266 4.5104073515263 4.51068015253975 4.51246215303508
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.5142758149152 4.51530065215549 4.51767435789641 4.51962281870974
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.52023374812337 4.52098675502872 4.52327341746194 4.52436114520151
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.52748450778764 4.52813266106782 4.53218888190359 4.53292119781984
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.53393525302879 4.53423642212744 4.53678076417845 4.53735399085653
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.54179574967178 4.54314182318193 4.54586383321461 4.54826548140842
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.55005306619435 4.5507798803179 4.55450402785387 4.55744832614744
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.55880145706655 4.55882122824497 4.56298413094639 4.56396854313732
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.56829992377831 4.57219368052594 4.58111858013466 4.58345512093612
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.58370242525781 4.58429254555671 4.58515107555631 4.59838717545434
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.59974518041978 4.60395370807776 4.60843286713623 4.61201711722678
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.6288464657099 4.62913802184356 4.63322983080051 4.63434175334271
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.64409697369741 4.64798316239864 4.64851480085378 4.64853259491437
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.65062001886756 4.65570808897287 4.65783999807754 4.65944408546626
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.66766922285488 4.6735341490265 4.67483885430807 4.67510378809911
## 0 0 0 0 1
## 1 1 1 1 0
## predict
## real 4.67615230537381 4.67717525761305 4.67963034298722 4.67994464972575
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.68006680810916 4.68008104335763 4.68417285231458 4.68636706813063
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.69042808904782 4.69047278566072 4.69071974664571 4.69145813828682
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.69479732035419 4.69562991739405 4.6980702568823 4.69855949042618
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.70176763082948 4.70581792031173 4.70631615400828 4.70649988847123
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.71151988550742 4.71495047229259 4.72281981096729 4.73001606476314
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.73293367196583 4.73420214099228 4.73791194328256 4.73968867430797
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.74482059518045 4.74633278096357 4.74686895469824 4.75099872431779
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.75755273627315 4.76450870150276 4.76526184590463 4.76826973342783
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.76978062981787 4.77010084041061 4.77060144664857 4.77175417178328
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.77806872205181 4.79342902029492 4.80128766878361 4.80469750958108
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.80878931853803 4.81028953849171 4.82168814915749 4.82968720098614
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.83689112429263 4.84819430132941 4.84827570453345 4.86085604280346
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.86628809016457 4.87255030702555 4.87391424334453 4.87771753059283
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.89616414440597 4.90082360970233 4.92234887980055 4.92330351835182
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.92702954841622 4.95940822135793 4.96192965539374 4.96225291416115
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 4.97751998703145 4.98718027135556 4.99385762796504 5.00798030022006
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.0260425708574 5.04978798969678 5.05129708605628 5.07081922447695
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.07242629129208 5.07830302517217 5.07898499333168 5.08063956597364
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.10355369613258 5.10516076294771 5.11071444430515 5.13354608740107
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.14734198332551 5.1555208561566 5.17105557744245 5.19100878089345
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.19274335118501 5.20170084573883 5.21709851150918 5.23706529519988
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.24303582696365 5.24957496928624 5.2546169184947 5.26312240631753
## 0 0 0 0 1
## 1 1 1 1 0
## predict
## real 5.27503014819659 5.28628961148892 5.28847087979142 5.28977853700011
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.30616593942993 5.30942989642984 5.31101141717584 5.31473848913963
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.3272303691654 5.33234513036159 5.3332406616223 5.3347872492559
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.33860443654439 5.3405287482755 5.34963755421777 5.36356506431717
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.36917141097417 5.36929794651615 5.36974429722731 5.37744399919104
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.38709040931609 5.40339635449075 5.40515267995343 5.40744176992665
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.41912511875167 5.42010320416549 5.4203783364714 5.42187865954746
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.42905118792919 5.43234541545658 5.43507800567807 5.43575227682763
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.43889230978771 5.44127621891951 5.44951023271395 5.45359515613256
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.45813211578033 5.45979906864164 5.47044313077237 5.47892797894773
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.49401310363597 5.4970458203461 5.49826505913825 5.50178279557136
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.50592680043938 5.50628062203547 5.50900849467345 5.51327790381132
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.51454925601664 5.51652596137059 5.52021847502107 5.53136865442877
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.53201244098106 5.5349489872661 5.53602532168711 5.54150387256649
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.5496143371202 5.55648140079512 5.55743614246875 5.55787110839431
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.57364527761735 5.57628820293629 5.5791247483074 5.58543369389773
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.58735150863925 5.6168593083243 5.6210667786751 5.62928005335666
## 0 0 0 0 1
## 1 1 1 1 0
## predict
## real 5.66796955275898 5.67069426200842 5.68495356685605 5.69123481904692
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.70768034874134 5.71236154174409 5.73652692625461 5.74168617260228
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.75089274275542 5.75240196286177 5.7529386472339 5.76639124153579
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.77232728895118 5.77605736784069 5.79120536487636 5.80268779206283
## 0 0 0 0 1
## 1 1 1 1 0
## predict
## real 5.80610432756165 5.81412015004407 5.82217423236513 5.82758746960741
## 0 0 1 0 0
## 1 1 0 1 1
## predict
## real 5.83579777861221 5.84057155572867 5.84772932447481 5.85522504287569
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 5.8576365766587 5.8767063780161 5.9198726312195 5.97596899522701
## 0 0 0 1 0
## 1 1 1 0 1
## predict
## real 5.99069354861921 6.00400086600855 6.06332379198944 6.1118971189957
## 0 0 1 1 0
## 1 1 0 0 1
## predict
## real 6.15318225622639 6.16750714638785 6.19088979847709 6.21130492374643
## 0 0 0 1 1
## 1 1 1 0 0
## predict
## real 6.215555692978 6.23029962212506 6.26896625570866 6.33037601250738
## 0 0 0 0 1
## 1 1 1 1 0
## predict
## real 6.35991706902116 6.43069943262292 6.45657274445787 6.48824154437919
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 6.51136540549238 6.54287236196022 6.57344823342031 6.59676761773854
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 6.78049476895071 6.79710495131254 6.7979363620817 6.82207803492772
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 6.82352005365188 6.83134291630761 6.83171256859608 6.83227552801104
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 6.85882426289212 6.86000535018473 6.86916349470034 6.87161858007451
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 6.87212264200222 6.87296274521506 6.88389993829891 6.89205389944516
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 6.90483931959726 6.91251294422993 6.91455884870841 6.93399494125394
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 6.9481650643372 6.95808621821943 6.96439590653312 6.96490707921023
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 6.97053361996855 6.97134307785482 6.97423995969426 6.99436098607606
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 7.01449782940062 7.02548720464585 7.05715741604895 7.10180655931728
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 7.11170102898346 7.11769049295474 7.11800242027702 7.12127033151262
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 7.12861028052674 7.15982665567019 7.1990404042267 7.21199779925707
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 7.22052240125071 7.2487529380014 7.25505076360802 7.2798565968033
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 7.28353924548903 7.3437422124632 7.3587306835882 7.42116339262616
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 7.42178784040606 7.48715419192572 7.52856151503914 7.56848532057816
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 7.63653190994541 7.68163550323321 7.92899545173169 8.17689385713356
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 8.20551991204235 8.31847835551553 8.40650365315397 8.42489899939967
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 8.49176746715108 8.59680835777349 8.61560932610277 8.63936317092581
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 8.70217243841505 8.74905926659265 8.88635722365973 8.90819383312696
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 8.94710991023414 8.97224665795781 9.02445182172123 9.05881510848827
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 9.22295002984388 9.42652642248262 9.45365096804465 9.47206410835094
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 9.53275335006705 9.58237295242826 9.66581932099585 9.73300293398292
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 9.77830222068827 9.81950022889549 9.84195772733049 9.90026897064385
## 0 0 0 0 0
## 1 1 1 2 1
## predict
## real 9.90681390160677 9.95455661556585 9.97069790807311 9.99489650128855
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 10.0308842741322 10.0329503452127 10.037480991833 10.0717276492717
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 10.1172771978471 10.1281827570455 10.1448880112763 10.1602322948648
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 10.1688482259532 10.276103443995 10.2826218678292 10.3096752577734
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 10.3290816935512 10.3948086094921 16.9456694998674 20.7525002251175
## 0 0 0 0 0
## 1 1 1 1 1
## predict
## real 25.8960170890924
## 0 0
## 1 1(table[1,1] + table[2,2]) / sum(table) # 정확도## [1] 0.0003752345- ROC & AUC
ROC(test = yhat_test, stat = test$Occupancy, plot = "ROC", AUC = T, main ="SVM")