SVM with cross validation in R using caret

r, r-caret, svm

Solution

You have to save your CV predictions via the "savePred" option in your `trainControl` object. I'm not sure what package your "cadets" data is from, but here is a trivial example using iris:

> library(caret)
> ctrl <- trainControl(method = "cv", savePred=T, classProb=T)
> mod <- train(Species~., data=iris, method = "svmLinear", trControl = ctrl)
> head(mod$pred)
        pred        obs      setosa  versicolor   virginica rowIndex   .C Resample
1     setosa     setosa 0.982533940 0.009013592 0.008452468       11 0.25   Fold01
2     setosa     setosa 0.955755054 0.032289120 0.011955826       35 0.25   Fold01
3     setosa     setosa 0.941292675 0.044903583 0.013803742       46 0.25   Fold01
4     setosa     setosa 0.983559919 0.008310323 0.008129757       49 0.25   Fold01
5     setosa     setosa 0.972285699 0.018109218 0.009605083       50 0.25   Fold01
6 versicolor versicolor 0.007223973 0.971168170 0.021607858       59 0.25   Fold01

EDIT: The "C" is one of tuning parameters for your SVM. Check out the help for the `ksvm` function in the kernlab package for more details.

EDIT2: Trivial regression example

> library(caret)
> ctrl <- trainControl(method = "cv", savePred=T)
> mod <- train(Sepal.Length~., data=iris, method = "svmLinear", trControl = ctrl)
> head(mod$pred)
      pred obs rowIndex   .C Resample
1 4.756119 4.8       13 0.25   Fold01
2 4.910948 4.8       31 0.25   Fold01
3 5.094275 4.9       38 0.25   Fold01
4 4.728503 4.8       46 0.25   Fold01
5 5.192965 5.3       49 0.25   Fold01
6 5.969479 5.9       62 0.25   Fold01

Problem

I was told to use the caret package in order to perform Support Vector Machine regression with 10 fold cross validation on a data set I have. I'm plotting my response variable against 151 variables. I did the following:- ``` > ctrl <- trainControl(method = "repeatedcv", repeats = 10) > set.seed(1500) > mod <- train(RT..seconds.~., data=cadets, method = "svmLinear", trControl = ctrl) ``` in which I got ``` C RMSE Rsquared RMSE SD Rsquared SD 0.2 50 0.8 20 0.1 0.5 60 0.7 20 0.2 1 60 0.7 20 0.2 ``` But I want to be able to have a look at my folds, and for each of them how close the predicted values were to the actual values. How do I go about looking at this? Also, it says that:- ``` RMSE was used to select the optimal model using the smallest value. The final value used for the model was C = 0. ``` I was just wondering what this meant and what the C stands for in the table above? ``` RT (seconds) 76_TI2 114_DECC 120_Lop 212_PCD 236_X3Av 38 4.086 1.2 2.322 0 0.195 40 2.732 0.815 1.837 1.113 0.13 41 4.049 1.153 2.117 2.354 0.094 41 4.049 1.153 2.117 3.838 0.117 42 4.56 1.224 2.128 2.38 0.246 42 2.96 0.909 1.686 0.972 0.138 42 3.237 0.96 1.922 1.202 0.143 44 2.989 0.8 1.761 2.034 0.11 44 1.993 0.5 1.5 0 0.102 44 2.957 0.8 1.761 0.988 0.141 44 2.597 0.889 1.888 1.916 0.114 44 2.428 0.691 1.436 1.848 0.089 ``` This is a snipet of my dataset. I'm trying to pot RT seconds against 151 variables. Thanks

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