Before building a more sophisticated lending model, it is important to hold out a portion of the loan data to simulate how well it will predict the outcomes of future loan applicants.

As depicted in the following image, you can use 75% of the observations for training and 25% for testing the model.

The sample() function can be used to generate a random sample of rows to include in the training set. Simply supply it the total number of observations and the number needed for training.

Use the resulting vector of row IDs to subset the loans into training and testing datasets. The dataset loans is available for you to use.

Growing larger classification trees

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