Skip to content

Update 10-resampling.Rmd #381

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Update 10-resampling.Rmd #381

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion 10-resampling.Rmd
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -96,7 +96,7 @@ all_res <-
tr_ratio <- round(all_res$train[1]/all_res$train[2])
```

Based on these results, the random forest is much more capable of predicting the sale prices; the RMSE estimate is `r xfun::numbers_to_words(tr_ratio)`-fold better than linear regression. If we needed to choose between these two models for this price prediction problem, we would probably chose the random forest because, on the log scale we are using, its RMSE is about half as large. The next step applies the random forest model to the test set for final verification:
Based on these results, the random forest is much more capable of predicting the sale prices; the RMSE estimate is `r xfun::numbers_to_words(tr_ratio)`-fold better than linear regression. If we needed to choose between these two models for this price prediction problem, we would probably choose the random forest because, on the log scale we are using, its RMSE is about half as large. The next step applies the random forest model to the test set for final verification:

```{r resampling-eval-test-rf}
estimate_perf(rf_fit, ames_test)
Expand Down