Exercise

# Using the recursive partitioning model architecture

In the previous exercise, you used the linear modeling architecture to construct a model of a runner's time as a function of age and sex. There are many different model architectures available. In this exercise, you'll build models using the recursive partitioning architecture and the same `Runners`

data frame as in the previous question. The model-building function to use is `rpart()`

, which is analogous to `lm()`

for linear models.

The recursive partitioning architecture has a parameter, `cp`

, that allows you to dial up or down the complexity of the model being built. Without worrying about the details just yet, you can set this parameter as a named argument to `rpart()`

. In later chapters, you'll work with tools for determining a good value for `cp`

. (If you're really curious about the nitty-gritty details of `cp`

, check out `?rpart.control`

.)

Instructions

**100 XP**

- Load the
`rpart`

package - Using the
`rpart()`

function (from the`rpart`

package) construct a model of net running time versus age and sex. Set the complexity parameter`cp = 0.002`

. Store the result as`model_2`

. - Use the command provided to examine a graph of the model.