DataCamp

Modeling an interaction (2)

In this exercise, you will compare the performance of the interaction model you fit in the previous exercise to the performance of a main-effects only model. Because this data set is small, we will use cross-validation to simulate making predictions on out-of-sample data.

You will begin to use the dplyr package to do calculations.

mutate() adds new columns to a tbl (a type of data frame)
group_by() specifies how rows are grouped in a tbl
summarize() computes summary statistics of a column

You will also use tidyr's gather() which takes multiple columns and collapses them into key-value pairs.

The data frame alcohol and the formulas fmla_add and fmla_interaction are in the workspace.

Use kWayCrossValidation() to create a splitting plan for a 3-fold cross validation.
- The first argument is the number of rows to be split.
- The second argument is the number of folds for the cross-validation.
- You can set the 3rd and 4th arguments of the function to NULL.
Examine and run the sample code to get the 3-fold cross-validation predictions of a model with no interactions and assign them to the column pred_add.
Get the 3-fold cross-validation predictions of the model with interactions. Assign the predictions to the column pred_interaction.
- The sample code shows you the procedure.
- Use the same splitPlan that you already created.
Fill in the blanks to
- gather the predictions into a single column pred.
- add a column of residuals (actual outcome - predicted outcome).
- get the RMSE of the cross-validation predictions for each model type.
Compare the RMSEs. Based on these results, which model should you use?

Take Hint (-30 XP)

Exercise

Modeling an interaction (2)

Instructions