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Random forest performance

It is now time to see whether the random forests models you built in the previous exercise are able to outperform the logistic regression model.

Remember that the validate recall for the logistic regression model was 0.43.

This exercise is part of the course

Machine Learning in the Tidyverse

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Exercise instructions

  • Prepare the validate_actual and validate_predicted columns for each mtry/fold combination.
  • Calculate the recall for each mtry/fold combination.
  • Calculate the mean recall for each value of mtry.

Hands-on interactive exercise

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

cv_prep_rf <- cv_models_rf %>% 
  mutate(
    # Prepare binary vector of actual Attrition values in validate
    validate_actual = map(validate, ~.x$___ == "___"),
    # Prepare binary vector of predicted Attrition values for validate
    validate_predicted = map2(.x = ___, .y = ___, ~predict(.x, .y, type = "response")$predictions == "Yes")
  )

# Calculate the validate recall for each cross validation fold
cv_perf_recall <- cv_prep_rf %>% 
  mutate(recall = map2_dbl(.x = ___, .y = ___, ~recall(actual = .x, predicted = .y)))

# Calculate the mean recall for each mtry used  
cv_perf_recall %>% 
  group_by(___) %>% 
  summarise(mean_recall = mean(___))
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