Extracting estimates with ranger decision trees - r

Question

I am getting the error message Error: No tidy method for objects of class ranger when trying to extract the estimates for a regression model built with the ranger package in R.
Here is my code:
# libraries
library(tidymodels)
library(textrecipes)
library(LiblineaR)
library(ranger)
library(tidytext)
# create the recipe
comments.rec <- recipe(year ~ comments, data = oa.comments) %>%
step_tokenize(comments, token = "ngrams", options = list(n = 2, n_min = 1)) %>%
step_tokenfilter(comments, max_tokens = 1e3) %>%
step_stopwords(comments, stopword_source = "stopwords-iso") %>%
step_tfidf(comments) %>%
step_normalize(all_predictors())
# workflow with recipe
comments.wf <- workflow() %>%
add_recipe(comments.rec)
# create the regression model using support vector machine
svm.spec <- svm_linear() %>%
set_engine("LiblineaR") %>%
set_mode("regression")
svm.fit <- comments.wf %>%
add_model(svm.spec) %>%
fit(data = oa.comments)
# extract the estimates for the support vector machine model
svm.fit %>%
pull_workflow_fit() %>%
tidy() %>%
arrange(-estimate)
Below is the table of estimates for each tokenized term in the data set (this is a dirty data set for demo purposes)
term estimate
<chr> <dbl>
1 Bias 2015.
2 tfidf_comments_2021 0.877
3 tfidf_comments_2019 0.851
4 tfidf_comments_2020 0.712
5 tfidf_comments_2018 0.641
6 tfidf_comments_https 0.596
7 tfidf_comments_plan s 0.462
8 tfidf_comments_plan 0.417
9 tfidf_comments_2017 0.399
10 tfidf_comments_libraries 0.286
However, when using the ranger engine to create a regression model from random forests, I have no such luck and get the error message above
# create the regression model using random forests
rf.spec <- rand_forest(trees = 50) %>%
set_engine("ranger") %>%
set_mode("regression")
rf.fit <- comments.wf %>%
add_model(rf.spec) %>%
fit(data = oa.comments)
# extract the estimates for the random forests model
rf.fit %>%
pull_workflow_fit() %>%
tidy() %>%
arrange(-estimate)

Answer 1

To put this back to you in a simpler form that I think highlights the issue - if you had a decision tree model, how would you produce coefficients on the data in the dataset? What would those mean?
I think what you are looking for here is some form a attribution to each column. There are tools to do this built into tidymodels, but you should read on what it's actually reporting.
For you, you can get a basic idea of what those numbers would look like by using the vip package, though the produced numbers are definitely not comparable directly to your svm ones.
install.packages('vip')
library(vip)
rf.fit %>%
pull_workflow_fit() %>%
vip(geom = "point") +
labs(title = "Random forest variable importance")
You'll produce a plot with relative importance scores. To get the numbers
rf.fit %>%
pull_workflow_fit() %>%
vi()
tidymodels has a decent walkthrough doing this here but, given you have a model that can estimate importance scores you should be good to go.
Tidymodels tutorial page - 'a case study'
edit: if you HAVEN'T done this you may need to rerun your initial model with a new parameter passed during the 'set_engine' step of your code that gives ranger an idea of what kind of importance scores you are looking for/how they should be computed.