R 앙상블_시계열, 배깅, 부스팅, 랜덤포레스트

앙상블_시계열, 배깅(bagging), 부스팅(boosting), 랜덤포레스트(randomForest)

###시계열 예측 이해와 활용 str(AirPassengers) AirPassengers plot(AirPassengers) apts<- ts(AirPassengers,frequency = 12) #frequency=12 : 월단위로 쪼개겠다 f<- decompose(apts) plot(f) install.packages('tseries') install.packages("forecast") library(tseries) library(forecast) plot(stl(AirPassengers,s.window='periodic')) #데이터르셋을 시즌, 트렌트,랜덤으로 분해 ##위 두 플랫모두 같은 걸 보여줌. 다른 방식임을 설명하는 것. adf.test(diff(log(AirPassengers)),alternative='stationary',k=0)  #diff로 차분하고 log로 변환 안정적여부 묻기 adf.test((AirPassengers),alternative='stationary',k=0) c/ auto.arima(AirPassengers) #arima pdq / p:AR모형의 p차수 d:차분차수 q:MA모형의 차수 fit<-arima(AirPassengers,order=c(2,1,1),list(order=c(0,1,0),period=12)) fore<- predict(fit,n.ahead=24) #24개월 데이터 예측 U<-fore$pred+2*fore$se L<-fore$pred-2*fore$se ts.plot(AirPassengers,fore$pred,U,L,col=c(1,2,4,4),lty=c(1,1,2,2)) legend('topleft',c('Actual','Forecast','Error Bounds (95% Confidence)'),col=c(1,2,4),lty = c(1,1,2,2)) #legend 범례 auto.arima(diff(log(AirPassengers),alternative=)) #영국 내 월별 폐질환 사망자에 관한 시계열 자료 ldeaths plot(ldeaths) ldeaths.decompose <- decompose(ldeaths) ldeaths.decompose$seasonal plot(ldeaths.decompose) ldeaths.decompose.adj <- ldeaths - ldeaths.decompose$seasonal plot(ldeaths.decompose.adj) Nile plot(Nile) Nile.diff1 <- diff(Nile, differences  = 1) #diff 차분 plot(Nile.diff1) Nile.diff2 <- diff(Nile, differences = 2) plot(Nile.diff2) acf(Nile.diff2,lag.max=20) acf(Nile.diff2,lag.max=20, plot=F) #lag.max 시계열 20등분하겠다 pacf(Nile.diff2,lag.max=20) pacf(Nile.diff2,lagmax=20,plot=F) auto.arima(Nile) Nile.arima <- arima(Nile,order=c(1,1,1)) Nile.arima Nile.forecast<- forecast(Nile.arima,h=10) Nile.forecast plot(Nile.forecast) airquality ozon=airquality$Ozone plot(ozon) ozon.diff <- diff(ozon,differences=1) plot(ozon.diff) auto.arima(ozon.diff) ozon.arima <- arima(ozon.diff,order=c(3,0,0)) ozon.arima ozon.fore <- forecast(ozon.arima,h=10) plot(ozon.fore) ###앙상블 모형 ##bagging install.packages('adabag') library(adabag) data(iris) iris.bagging <- bagging(Species~.,data=iris,mfinal=10) #mfinal 반복수(모형갯수), 디폴트=100 iris.bagging$importance #중요 비율을 보여줌  plot(iris.bagging$trees[[10]]) #trees : 배깅기법으로 만든 의사결정나무 / 10번째 tree 값 text(iris.bagging$trees[[10]]) pred <- predict(iris.bagging,newdata=iris) #bagging으로 예측 table(pred$class,iris[,5]) ##부스팅 boo.adabag <- boosting(Species~.,data=iris,boos=T, mfinal=10) boo.adabag$importance plot(boo.adabag$tree[[8]]) text(boo.adabag$tree[[8]]) pred <- predict(boo.adabag,newdata=iris) tb <- table(pred$class, iris[,5]) tb #오분류율 1-(sum(diag(tb))/sum(tb)) ##nnet함수로 분석 install.packages('ada') library(ada) iris <- iris[iris$Species!="setosa",] n <-dim(iris)[1] trind <-sample(1:100, floor(.6*100), FALSE) teind <- setdiff(1:100, trind) #set difference 차집합 iris[,5]<-as.factor((levels(iris[,5])[2:3])[as.numeric(iris[,5])-1]) gdis <- ada(Species~., data=iris[trind,],iter=20,nu=1,type='discrete') #iter 학습횟수 nu부스팅을 위한 축소모수 gdis <- addtest(gdis,iris[teind,-5],iris[teind,5]) gdis plot(gdis,TRUE,TRUE) pairs(gdis,iris[trind,-5],maxvar=4) ##랜덤포레스트 install.packages('randomForest') library(randomForest) stagec=na.omit(stagec) t_index=sample(1:nrow(stagec),size=nrow(stagec)*0.7) train=stagec[t_index,] test=stagec[-t_index,] rf <- randomForest(ploidy~.,data=train,ntree=100,proximity=TRUE) table(predict(rf),train$ploidy) print(rf) plot(rf) importance(rf) varImpPlot(rf) rf.pred <- predict(rf,newdata=test) table(rf.pred,test$ploidy) plot(margin(rf)) #마진이랑 ROC랑 비슷 library(e1071) library(Epi) ROC(test=rf.pred,stat=test$ploidy,plot="ROC",AUC=T) install.packages('party') library(party) #랜덤포레스트는 party 패키지의 cforest()로도 이용할 수 있음. set.seed(1234) #set.seed 랜덤 고정 cf <- cforest(ploidy~.,data=train) cf.pred <- predict(cf,newdata=test, OOB=T, type='response')