Input transforms: the "hockey stick" (2)

In the last exercise you saw that a quadratic model seems to fit the houseprice data better than a linear model. In this exercise you will confirm whether the quadratic model would perform better on out-of-sample data. Since this data set is small, you will use cross-validation. The quadratic formula fmla_sqr that you created in the last exercise is in your workspace.

For comparison, the sample code will calculate cross-validation predictions from a linear model price ~ size.

The data frame houseprice and the formula fmla_sqr from the last exercise are in the workspace.

Use kWayCrossValidation() to create a splitting plan for a 3-fold 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 the model price ~ size and add them to the column pred_lin.
Get the cross-validation predictions for price as a function of squared size. Assign them to the column pred_sqr.
- The sample code gives you the procedure.
- You can use the splitting plan you already created.
Fill in the blanks to gather the predictions and calculate the residuals.
Fill in the blanks to compare the RMSE for the two models. Which one fits better?

Exercise

Input transforms: the "hockey stick" (2)

Instructions