Preparing for evaluation

In order to measure the validate performance of your models you need compare the predicted values of life_expectancy for the observations from validate set to the actual values recorded. Here you will prepare both of these vectors for each partition.

This exercise is part of the course

Machine Learning in the Tidyverse

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Exercise instructions

Extract the actual life_expectancy from the validate data frames and store these in the column validate_actual.
Predict the life_expectancy for each validate partition using the map2() and predict() functions in the column validate_predicted.

Hands-on interactive exercise

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

cv_prep_lm <- cv_models_lm %>% 
  mutate(
    # Extract the recorded life expectancy for the records in the validate data frames
    validate_actual = map(validate, ~.x$___),
    # Predict life expectancy for each validate set using its corresponding model
    validate_predicted = map2(.x = model, .y = validate, ~___(.x, .y))
  )

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