Add engine specification field for predictor encodings #319
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Merge branch 'master' into encoding-options # Conflicts: # R/linear_reg_data.R # R/svm_poly_data.R # R/svm_rbf_data.R # tests/testthat/test_svm_poly.R # tests/testthat/test_svm_rbf.R
Member
Author
|
This PR also fixes the function used with Spark decision trees 🌳 for regression. |
topepo
reviewed
May 26, 2020
This was referenced Jun 2, 2020
Contributor
|
This pull request has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue. |
Sign up for free
to subscribe to this conversation on GitHub.
Already have an account?
Sign in.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Closes #290.
This PR adds the field for engine-specific encodings (only dummy/indicator variables for now).
For example, the encoding options for ranger are:
While the encoding options for vanilla logistic regression are:
These changes depend on handling the names for data arguments implemented in #315 and #316.
These encodings can be used in workflows so that the user experiences the same behavior around dummy variable creation in both parsnip and workflows.
I am pretty confident that
predictor_indicators = TRUE / FALSEis correct for all the model + engine combinations except for liquidSVM. I was having trouble getting output from those models and could use some double-checking.