Model performance metrics
Evaluating model results is an important step in the modeling process. Model evaluation should be done on the test dataset in order to see how well a model will generalize to new datasets.
In the previous exercise, you trained a linear regression model to predict selling_price using home_age and sqft_living as predictor variables. You then created the home_test_results tibble using your trained model on the home_test data.
In this exercise, you will calculate the RMSE and R squared metrics using your results in home_test_results.
The home_test_results tibble has been loaded into your session.
Diese Übung ist Teil des Kurses
Modeling with tidymodels in R
Anleitung zur Übung
- Execute the first two lines of code which print the
home_test_results. This tibble contains the actual and predicted home selling prices in thehome_testdataset. - Using
home_test_results, calculate the RMSE and R squared metrics.
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# Print home_test_results
home_test_results
# Calculate the RMSE metric
home_test_results %>%
___(___, ___)
# Calculate the R squared metric
home_test_results %>%
___(___, ___)