Exercise

# Using PCA as an alternative to nearZeroVar()

An alternative to removing low-variance predictors is to run PCA on your dataset. This is sometimes preferable because it does not throw out all of your data: many different low variance predictors may end up combined into one high variance PCA variable, which might have a positive impact on your model's accuracy.

This is an especially good trick for linear models: the `pca`

option in the `preProcess`

argument will center and scale your data, combine low variance variables, and ensure that all of your predictors are orthogonal. This creates an ideal dataset for linear regression modeling, and can often improve the accuracy of your models.

Instructions

**100 XP**

`bloodbrain_x`

and `bloodbrain_y`

are loaded in your workspace.

- Fit a
`glm`

model to the full blood-brain dataset using the`"pca"`

option to`preProcess`

. - Print the model to the console and inspect the result.