tutorial_regression2

Question 1.8 {points: 1} Calculate the to assess goodness of fit on credit_fit (remember this is how well it predicts on the training data used to fit the model). Return a single numerical value named lm_rmse . set.seed ( 2020 ) # DO NOT REMOVE #... <- credit_fit |> # predict(...) |> # bind_cols(...) |> # ...(truth = ..., estimate = ...) |> # filter(.metric == ...) |> # select(...) |> # pull() ### BEGIN SOLUTION lm_rmse <- credit_fit |> predict ( credit_training ) |> bind_cols ( credit_training ) |> metrics ( truth = Balance , estimate = .pred ) |> filter ( .metric == 'rmse' ) |> select ( .estimate ) |> pull () ### END SOLUTION lm_rmse test_1.8 () Question 1.9 {points: 1} Calculate using the test data. Return a single numerical value named lm_rmspe . set.seed ( 2020 ) # DO NOT REMOVE ### BEGIN SOLUTION lm_rmspe <- credit_fit |> predict ( credit_testing ) |> bind_cols ( credit_testing ) |> metrics ( truth = Balance , estimate = .pred ) |> filter ( .metric == 'rmse' ) |> select ( .estimate ) |> pull () ### END SOLUTION lm_rmspe test_1.9 () Question 1.9.1 {points: 3} In [ ]: In [ ]: In [ ]: In [ ]: