Random forest is another useful ensemble learning method that grows multiple decision trees during the training process. Each decision tree will output its own prediction results corresponding to the input. The forest will use the voting mechanism to select the most voted class as the prediction result. In this recipe, we will illustrate how to classify data using the
In this recipe, we will continue to use the telecom
churn dataset as the input data source to perform classifications with the random forest method.
Perform the following steps to classify data with random forest:
> install.packages("randomForest") ...