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.

This exercise is part of the course

Modeling with tidymodels in R

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Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Print home_test_results
home_test_results

# Calculate the RMSE metric
home_test_results %>% 
  ___(___, ___)

# Calculate the R squared metric
home_test_results %>% 
  ___(___, ___)

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