R Data Analysis Cookbook, Second Edition - Second Edition

How it works...

Step 1 loads the necessary packages and step 2 reads the data and converts the response variable to a factor.

Step 3 sets aside some of the data for later use. Strictly speaking, we do not have to partition the data for random forests because, while building each tree, the method sets aside some of the cases for cross-validation. However, we set aside some of the cases just to illustrate the process of using the model for prediction. (We set the random seed to enable you to match your results with those that we display.)

Step 4 uses the randomForest function to build the model. As the predictor variables are in the first four variables of the data frame and we want to use only the selected subset for model building, we specify ...