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:
\(RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n}(y_i - \hat{y}_i)^2}\)
\(MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|\)
Cet exercice fait partie du cours
Practicing Statistics Interview Questions in R
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de 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(___)