embed 1.0.0

• step_collapse_cart() can pool a predictor’s factor levels using a tree-based method.

• step_collapse_stringdist() can pool a predictor’s factor levels using string distances.

• Case weights support have been added to step_discretize_cart(), step_discretize_xgb(), step_lencode_bayes(), step_lencode_glm(), and step_lencode_mixed().

embed 0.2.0

• step_embed() now correctly defaults to have a random id with the word “embed”. (#102)

• step_feature_hash() is soft deprecated in embed in favor of step_dummy_hash() in textrecipes. (#95)

• Steps now have a dedicated subsection detailing what happens when tidy() is applied. (#105)

• Reorganize documentation for all recipe step tidy methods (#115).

• Fixed a bug where woe_table() and step_woe() didn’t respect the factor levels of the outcome. (109)

embed 0.1.5

• Re-licensed package from GPL-2 to MIT. See consent from copyright holders here.

• The tunable parameter ranges for step_umap() were changed for neighbors, num_comp, and min_dist to prevent uwot segmentation faults. The step also check to see if the data dimensions are consistent with the argument values.

• Two new PCA steps were added, each using sparse techniques for estimation: step_pca_sparse() and step_pca_sparse_bayes().

• Updated to use recipes_eval_select() from recipes 0.1.17 (#85).

• Added prefix argument to step_umap() to harmonize with other recipes steps (#93).

• All embed recipe steps now officially support empty selections to be more aligned with recipes, dplyr and other packages that use tidyselect.

• step_woe() no longer warns about high-cardinality predictors when the recipe is estimated. Instead it warns when categories have fewer than 10 data points in the training set. (#74)

embed 0.1.4

• Minor release with changes to test for cases when CRAN cannot get xgboost to work on their Solaris configuration.

• lme4 and rstanarm are now in the Suggests list so they are not automatically installed with embed. A message is written to the console if those packages are missing and their associated steps functions are invoked.

embed 0.1.3

• More changes to enable better parallel processing on windows.

embed 0.1.2

• Changes to enable better parallel processing on windows.

embed 0.1.1

• Changes to tests to get out of archive jail.

• Updated the plumbing behind step_woe().

• Due to a bug in tensorflow, added a “warm start” to instigate a TF session if one does not currently exist.

embed 0.1.0

• Changes for dplyr 1.0.0

New Steps

• step_discretize_xgb() and step_discretize_cart() can be used to convert numeric predictors to categorical using supervised binning methods based on tree models. Thanks to Konrad Semsch for the contribution.

• Added step_feature_hash() for creating dummy variables using feature hashing.

Breaking Changes

• tidy.step_woe() now has column names consistent with other recipe steps.

Bug fixes

• Fixed a bug in detecting the TF version.

embed 0.0.6

• Small changes for base R’s stringsAsFactors change.

embed 0.0.5

• The example data are now in the modeldata package.

• Small TF updates to step_embed().

embed 0.0.4

• Methods were added for a future generic called tunable(). This outlines which parameters in a step can/could be tuned.

• Small updates to work with different versions of tidyr.

embed 0.0.3

New Steps

• step_umap() was added for both supervised and unsupervised encodings.
• step_woe() created weight of evidence encodings.

embed 0.0.2

A mostly maintainence release to be compatible with version 0.1.3 of recipes.

Other Changes:

• The package now depends on the generics pacakge to get the broom tidy methods.

• Karim Lahrichi added the ability to use callbacks when fitting tensorflow models. PR

embed 0.0.1

First CRAN version