Machine Learning with R Cookbook - Second Edition

How to do it...

Perform the following steps to calculate and classify data with the gradient boosting method:

1. First, install and load the package gbm:

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

2. The gbm function only uses responses ranging from 0 to 1; therefore, you should transform yes/no responses to numeric responses (0/1):

        > trainset$churn = ifelse(trainset$churn == "yes", 1, 0)   

3. Next, you can use the gbm function to train a training dataset:

        > set.seed(2)
        > churn.gbm = gbm(formula = churn ~ .,distribution = "bernoulli"        ,data = trainset,n.trees = 1000,interaction.depth = 7,shrinkage         = 0.01, cv.folds=3)

4. Then, you can obtain the summary information from the fitted model:

        > summary(churn.gbm)      

Relative influence plot of a fitted ...