classfication based on Feature 14

## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2
## Installing github repo(s) caretEnsemble/master from zachmayer
## Installing caretEnsemble.zip from https://github.com/zachmayer/caretEnsemble/archive/master.zip
## Installing caretEnsemble
## '/usr/lib/R/bin/R' --vanilla CMD INSTALL  \
##   '/tmp/Rtmp4rsny7/caretEnsemble-master'  \
##   --library='/opt/home/gong/R/x86_64-pc-linux-gnu-library/3.1'  \
##   --with-keep.source 
## 
## Reloading installed caretEnsemble
## Loading required package: foreach
## Loading required package: iterators
## Loading required package: parallel

• There are 1798 samples for training

• There are 927 samples for testing

• There are 1044 samples belong to “l” level

• There are 1681 samples are “h” level

# class_ensemble_function(inputsTrain,targetsTrainClass,inputsTest,targetsTestClass,14)

The ROC results by classification

###############training set#####################
load(paste("dataset_",14,"predsTrainClass.RData"))
resultsTrain<-sort(data.frame(colAUC(predsTrainClass, targetsTrainClass)))
resultsTrain

##          rpart svmRadial    gbm ENS_linear ENS_greedy   nnet treebag rf
## h vs. l 0.7741    0.8449 0.8558     0.8597     0.8724 0.8805       1  1

###############testing set#####################
load(paste("dataset_",14,"predsTestClass.RData"))
resultsTest<-sort(data.frame(colAUC(predsTestClass, targetsTestClass)))
resultsTest

##          rpart treebag    gbm     rf   nnet svmRadial ENS_greedy
## h vs. l 0.7205  0.7804 0.8089 0.8094 0.8114     0.817     0.8171
##         ENS_linear
## h vs. l     0.8198

The ROC results by regression

source('~/functions/calculateErrors.R', echo=FALSE)
load("~/PED/regression/dataset_ _14_ _svmFit.RData")
load("~/PED/regression/dataset_ _14_ _rpartFit.RData")
load("~/PED/regression/dataset_ _14_ _rfFit.RData")
load("~/PED/regression/dataset_ _14_ _nnetFit.RData")
load("~/PED/regression/dataset_ _14_ _lmFit.RData")
load("~/PED/regression/dataset_ _14_ _linearFit.RData")
load("~/PED/regression/dataset_ _14_ _greedyFit.RData")
load("~/PED/regression/dataset_ _14_ _gbmFit.RData")
load("~/PED/regression/dataset_ _14_ _bagTreeFit.RData")
models<-list(lmFit=lmFit,nnetFit=nnetFit,rfFit=rfFit,rpartFit=rpartFit,gbmFit=gbmFit,svmFit=svmFit,bagTreeFit=bagTreeFit,linearFit=linearFit)
##################traing resultsf################
sapply(models,function(model) predict(model, inputsTrain))->predTrain

## Loading required package: nnet
## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
## Loading required package: rpart
## Loading required package: gbm
## Loading required package: survival
## Loading required package: splines
## 
## Attaching package: 'survival'
## 
## The following object is masked from 'package:caret':
## 
##     cluster
## 
## Loaded gbm 2.1
## Loading required package: plyr
## Loading required package: kernlab
## Loading required package: ipred

resultsTrainReg<-sort(data.frame(colAUC(predTrain, targetsTrainClass)))
resultsTrainReg

##         rpartFit  lmFit bagTreeFit nnetFit svmFit gbmFit linearFit  rfFit
## h vs. l   0.7368 0.8348     0.8351  0.8492 0.8694  0.871    0.8828 0.9835

################test results#####################
sapply(models,function(model) predict(model, inputsTest))->predTest
resultsTestReg<-sort(data.frame(colAUC(predTest, targetsTestClass)))
resultsTestReg

##         rpartFit bagTreeFit gbmFit  lmFit  rfFit nnetFit linearFit svmFit
## h vs. l   0.7011     0.7844 0.7951 0.8007 0.8046  0.8173    0.8211  0.822