Regression evaluation
The test_set and model objects that you have derived in the previous exercise are available in your environment.
It's useful to present the accuracy of predictions with one number. You can then easily compare several models and show the progress to your employer or future employer.
Root Mean Squared Error and Mean Absolute Error are widely used to evaluate the regression models. Recall that their formulas are:
\(RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n}(y_i - \hat{y}_i)^2}\)
\(MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|\)
This exercise is part of the course
Practicing Statistics Interview Questions in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Assign Hwt from the test set to y
___ <- test_set$___
# Predict Hwt on the test set
___ <- ___(model, newdata = ___)
# Derive the test set's size
___ <- nrow(___)