When I run an SVM with ksvm from the kernlab package, all the outputs from the predict command on my final model are scaled. I know this is because I initiate scaled = T but I also know scaling your data is preferred in SVM modeling. How can I easily tell ksvm to return non-scaled predictions? If not, is there a way to just manipulate the predicted scaled values to raw values? Thank you, code is below:
svm1 <- ksvm(Y ~ 1
+ X1
+ X2
, data = data_nn
, scaled=T
, type = "eps-svr"
, kernel="anovadot"
, epsilon = svm1_CV2$bestTune$epsilon
, C = svm1_CV2$bestTune$C
, kpar = list(sigma = svm1_CV2$bestTune$sigma
, degree= svm1_CV2$bestTune$degree)
)
#Analyze Results
data_nn$svm_pred <- predict(svm1)
From the documentation:
argument scaled:
A logical vector indicating the variables to be scaled. If scaled is of length 1,
the value is recycled as many times as needed and all non-binary variables are scaled.
Per default, data are scaled internally (both x and y variables) to zero mean and
unit variance. The center and scale values are returned and used for later predictions.
SOLUTION NO.1
Let's see the following example:
#make random data set
y <- runif(100,100,1000) #the response variable takes values between 100 and 1000
x1 <- runif(100,100,500)
x2 <- runif(100,100,500)
df <- data.frame(y,x1,x2)
Typing this:
svm1 <- ksvm( y~1+x2+x2,data=df,scaled=T,type='eps-svr',kernel='anovadot')
> predict(svm1)
[,1]
[1,] 0.290848927
[2,] -0.206473246
[3,] -0.076651875
[4,] 0.088779924
[5,] 0.036257375
[6,] 0.206106048
[7,] -0.189082081
[8,] 0.245768175
[9,] 0.206742751
[10,] -0.238471569
[11,] 0.349902743
[12,] -0.199938921
Makes scaled predictions.
But if you change it to the following according to the documentation from above:
svm1 <- ksvm( y~1+x2+x2,data=df,scaled=c(F,T,T,T),type='eps-svr',kernel='anovadot')
#I am using a logical vector here so predictions will be raw data.
#only the intercept x1 and x2 will be scaled using the above.
#btw scaling the intercept (number 1 in the formula), actually eliminates the intercept.
> predict(svm1)
[,1]
[1,] 601.2630
[2,] 599.7238
[3,] 599.7287
[4,] 599.9112
[5,] 601.6950
[6,] 599.8382
[7,] 599.8623
[8,] 599.7287
[9,] 601.8496
[10,] 599.0759
[11,] 601.7348
[12,] 601.7249
As you can see this is raw data predictions.
SOLUTION NO.2
If you want to scale the y variable in the model you ll need to unscale the predictions yourself.
Before the model:
Calculate the mean and std before running the model:
y2 <- scale(y)
y_mean <- attributes(y2)$'scaled:center' #the mean
y_std <- attributes(y2)$'scaled:scale' #the standard deviation
Convert the predictions to raw:
svm1 <- ksvm( y~1+x2+x2,data=df,scaled=T,type='eps-svr',kernel='anovadot')
> predict(svm1) * y_std + y_mean
[,1]
[1,] 654.3604
[2,] 522.3578
[3,] 556.8159
[4,] 600.7259
[5,] 586.7850
[6,] 631.8674
[7,] 526.9739
[8,] 642.3948
[9,] 632.0364
[10,] 513.8646
[11,] 670.0349
[12,] 524.0922
[13,] 673.7202
And you got raw predictions!