Random forest model
In this exercise, you will use the randomForest()
function in the randomForest
package to build a random forest model for predicting churn of the customers in the training data set, training_set
. The target variable is called Future
.
You will also inspect and visualize the importance of the variables in the model.
This exercise is part of the course
Predictive Analytics using Networked Data in R
Exercise instructions
- Load the
randomForest
package. - Use the
set.seed()
function with the seed 863. - Build a random forest using the function
randomForest()
and all the variables intraining_set
. The response variableFuture
needs to be a factor, so utilize theas.factor()
function. - Plot the variable importance of the random forest model using
varImpPlot()
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Load package
___(randomForest)
# Set seed
set.seed(___)
# Build model
rfModel <- ___(as.factor(___)~. ,data=training_set)
# Plot variable importance
varImpPlot(___)