Partitioning into training and test data

Next, we will generate test and training datasets so that we can validate any models produced. There are many ways of generating test and training sets.

In earlier chapters, we used the createDataPartition function. For this example, we will generate the test and training data using native R functions. Please refer to the outline of the code here, and then run the code that follows:

• Set a variable corresponding to the percentage of the data to designate as training data (TrainingRows). In this example, we will use 75%.
• Use the sample() function to randomize the rows and assign to a new dataframe named ChurnStudy.
• Then select the first TrainingRows rows. Since the df dataframe has already been sampled, selecting a percentage of rows sequentially from a random sample is a convenient and valid way to select a training sample.
• The remaining rows (TrainingRows+1 to the end) will be the testing dataset. Assign it to ChurnStudy.test.

Once we have generated the...