Preventing overgrown trees

The tree grown on the full set of applicant data grew to be extremely large and extremely complex, with hundreds of splits and leaf nodes containing only a handful of applicants. This tree would be almost impossible for a loan officer to interpret.

Using the pre-pruning methods for early stopping, you can prevent a tree from growing too large and complex. See how the rpart control options for maximum tree depth and minimum split count impact the resulting tree.

rpart has been pre-loaded.

1
- Use rpart() to build a loan model using the training dataset and all of the available predictors.
  
  Set the model controls using rpart.control() with parameters cp set to 0 and maxdepth set to 6.
- See how the test set accuracy of the simpler model compares to the original accuracy of 58.3%.
  
  First create a vector of predictions using the predict() function.
  
  Compare the predictions to the actual outcomes and use mean() to calculate the accuracy.
2
In the model controls, remove maxdepth and add a minimum split parameter, minsplit, set to 500.

Exercise

Preventing overgrown trees

Instructions 1/2