# Testing variable importance in prediction

I'm currently looking at patient data and trying to use it to predict the importance of variables in prediction of response to a drug. I don't have many patients but have a lot of variables which I know is less than ideal.

I've formed a svm ensemble and a random forest model containing all the variables. I have a second dataset with which which I'm trying to test the validity of my models and roughly how important each variable is in prediction. Is it valid to just set all all of the values corresponding to a particular variable to 0 or should I shuffle the values within the column?

## 1 Answer

I have used the randomForest package in R several times and there were some functions to measure the variable importance such as importance() and varImpPlot(). As far as I know varImpPlot visualizes the the importance of each predictor with respect to variables' contribution in the decrease of error measures (e.g mean squared error for regression, Gini index for classification etc.)

What I usually do to measure variable importance in a really simple way is, I estimate linear and lasso regressions and then see how much the coefficients were shrunk.

library(MASS)
library(randomForest)
library(glmnet)
data(Boston)

# Random forest (based on the lab example from the book "An Introduction to Statistical Learning")
rf.boston <- randomForest(medv ~., data = Boston, mtry = 13, ntree = 25, importance = TRUE)
importance(rf.boston)
%IncMSE IncNodePurity
crim     2.80848132    1340.74773
zn       2.12135233      34.74243
indus    1.49676063     270.12398
chas     0.06577971      31.42226
nox      7.42985606    1381.58615
rm      13.41323143   18128.73241
age      6.28896854     487.95644
dis      7.08361676    2621.61526
rad      1.71445398     128.88846
tax      6.48150760     557.31305
ptratio  5.24860362     660.97934
black    2.00139088     562.16876
lstat    9.26159315   16553.01919

varImpPlot(rf.boston) # Linear model
lm.boston <- lm(formula = medv~., data = Boston)

# Lasso
optim.lambda <- cv.glmnet(x = as.matrix(Boston[, -14]), y = as.vector(Boston[, 14]))\$lambda.1se

lasso.boston<- glmnet(x = as.matrix(Boston[, -14]), y = as.vector(Boston[, 14]),
lambda = optim.lambda)

sum.abs <- abs(coef(lasso.boston)[-1])/ abs(coef(lm.boston)[-1])
sum.abs <- sum.abs[order(sum.abs, decreasing = F)]
barplot(sum.abs, horiz = T, col = "red", las=2) par(mfrow = c(2,1))
rf.boston <- randomForest(medv ~., data = Boston, mtry = 13, ntree = 25, importance = FALSE)
varImpPlot(rf.boston, main = "Variable importance (Random forest)")
barplot(sum.abs, horiz = T, col = "red", las=2, main = "Variable importance (Lasso)")


And a quick comparison for both: As far as I understand, Lasso approach could be a bit problematic when predictors are correlated. You can see that rm variable has a larger lasso coefficient than the linear one.

• Thanks for the well thought out and detaled response. I'll have a look into it although I will confess I'm slightly iffy on random forest as it performed way worse than it's inbuilt error validation claimed it would on a set of data I'd reserved for validation. – Sethzard Apr 20 '17 at 9:28