Exercise

# Fit a random forest

As you saw in the video, random forest models are much more flexible than linear models, and can model complicated nonlinear effects as well as automatically capture interactions between variables. They tend to give very good results on real world data, so let's try one out on the wine quality dataset, where the goal is to predict the human-evaluated quality of a batch of wine, given some of the machine-measured chemical and physical properties of that batch.

Fitting a random forest model is exactly the same as fitting a generalized linear regression model, as you did in the previous chapter. You simply change the `method`

argument in the `train`

function to be `"ranger"`

. The `ranger`

package is a rewrite of R's classic `randomForest`

package and fits models much faster, but gives almost exactly the same results. We suggest that all beginners use the `ranger`

package for random forest modeling.

Instructions

**100 XP**

- Train a random forest called
`model`

on the wine quality dataset,`wine`

, such that`quality`

is the response variable and all other variables are explanatory variables. - Use
`method = "ranger"`

. - Use a
`tuneLength`

of 1. - Use 5 CV folds.
- Print
`model`

to the console.