Preprocessing

library(caret); library(kernlab); data(spam)
inTrain <- createDataPartition(y=spam$type,
                              p=0.75, list=FALSE)
training <- spam[inTrain,]
testing <- spam[-inTrain,]
hist(training$capitalAve,main="",xlab="ave. capital run length")

mean(training$capitalAve)

[1] 4.709

sd(training$capitalAve)

[1] 25.48

trainCapAve <- training$capitalAve
trainCapAveS <- (trainCapAve  - mean(trainCapAve))/sd(trainCapAve) 
mean(trainCapAveS)

[1] 5.862e-18

sd(trainCapAveS)

[1] 1

testCapAve <- testing$capitalAve
testCapAveS <- (testCapAve  - mean(trainCapAve))/sd(trainCapAve) 
mean(testCapAveS)

[1] 0.07579

sd(testCapAveS)

[1] 1.79

preObj <- preProcess(training[,-58],method=c("center","scale"))
trainCapAveS <- predict(preObj,training[,-58])$capitalAve
mean(trainCapAveS)

[1] 5.862e-18

sd(trainCapAveS)

[1] 1

testCapAveS <- predict(preObj,testing[,-58])$capitalAve
mean(testCapAveS)

[1] 0.07579

sd(testCapAveS)

[1] 1.79

set.seed(32343)
modelFit <- train(type ~.,data=training,
                  preProcess=c("center","scale"),method="glm")
modelFit

3451 samples
  57 predictors
   2 classes: 'nonspam', 'spam' 

Pre-processing: centered, scaled 
Resampling: Bootstrap (25 reps) 

Summary of sample sizes: 3451, 3451, 3451, 3451, 3451, 3451, ... 

Resampling results

  Accuracy  Kappa  Accuracy SD  Kappa SD
  0.9       0.8    0.007        0.01    

preObj <- preProcess(training[,-58],method=c("BoxCox"))
trainCapAveS <- predict(preObj,training[,-58])$capitalAve
par(mfrow=c(1,2)); hist(trainCapAveS); qqnorm(trainCapAveS)

set.seed(13343)

# Make some values NA
training$capAve <- training$capitalAve
selectNA <- rbinom(dim(training)[1],size=1,prob=0.05)==1
training$capAve[selectNA] <- NA

# Impute and standardize
preObj <- preProcess(training[,-58],method="knnImpute")
capAve <- predict(preObj,training[,-58])$capAve

# Standardize true values
capAveTruth <- training$capitalAve
capAveTruth <- (capAveTruth-mean(capAveTruth))/sd(capAveTruth)

quantile(capAve - capAveTruth)

        0%        25%        50%        75%       100% 
-1.1324388 -0.0030842 -0.0015074 -0.0007467  0.2155789 

quantile((capAve - capAveTruth)[selectNA])

        0%        25%        50%        75%       100% 
-0.9243043 -0.0125489 -0.0001968  0.0194524  0.2155789 

quantile((capAve - capAveTruth)[!selectNA])

        0%        25%        50%        75%       100% 
-1.1324388 -0.0030033 -0.0015115 -0.0007938 -0.0001968 

• Training and test must be processed in the same way
• Test transformations will likely be imperfect
  • Especially if the test/training sets collected at different times
• Careful when transforming factor variables!
• preprocessing with caret