Session Ready
Exercise

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.

Instructions
100 XP
  • Execute the first two lines of code which print the home_test_results. This tibble contains the actual and predicted home selling prices in the home_test dataset.
  • Using home_test_results, calculate the RMSE and R squared metrics.