R: Recipes for Analysis, Visualization and Machine Learning

Ranking the variable importance with the rminer package

Besides using the caret package to generate variable importance, you can use the rminer package to generate the variable importance of a classification model. In the following recipe, we will illustrate how to use rminer to obtain the variable importance of a fitted model.

Getting ready

In this recipe, we will continue to use the telecom churn dataset as the input data source to rank the variable importance.

How to do it...

Perform the following steps to rank the variable importance with rminer:

1. Install and load the package, rminer:

   > install.packages("rminer")
   > library(rminer)
   

2. Fit the svm model with the training set:

   > model=fit(churn~.,trainset,model="svm")
   

3. Use the Importance function to obtain ...