Variable importance for support vector machine and naive Bayes classifiers in R

Question

I’m working on building predictive classifiers in R on a cancer dataset. I’m using random forest, support vector machine and naive Bayes classifiers. I’m unable to calculate variable importance on SVM and NB models

I end up receiving the following error.

Error in UseMethod("varImp") : no applicable method for 'varImp' applied to an object of class "c('svm.formula', 'svm')"

I would greatly appreciate it if anyone could help me.

Answer 1

Given

library(e1071)
model <- svm(Species ~ ., data = iris)
class(model)
# [1] "svm.formula" "svm"     

library(caret)
varImp(model)
# Error in UseMethod("varImp") : 
#   no applicable method for 'varImp' applied to an object of class "c('svm.formula', 'svm')"

methods(varImp)
#  [1] varImp.bagEarth      varImp.bagFDA        varImp.C5.0*         varImp.classbagg*   
#  [5] varImp.cubist*       varImp.dsa*          varImp.earth*        varImp.fda*         
#  [9] varImp.gafs*         varImp.gam*          varImp.gbm*          varImp.glm*         
# [13] varImp.glmnet*       varImp.JRip*         varImp.lm*           varImp.multinom*    
# [17] varImp.mvr*          varImp.nnet*         varImp.pamrtrained*  varImp.PART*        
# [21] varImp.plsda         varImp.randomForest* varImp.RandomForest* varImp.regbagg*     
# [25] varImp.rfe*          varImp.rpart*        varImp.RRF*          varImp.safs*        
# [29] varImp.sbf*          varImp.train*  

There is no function varImp.svm in methods(varImp), therefore the error. You might want to have a look at this post on Cross Validated, too.