estimate_orbital_size() is a new function that quickly
estimates the character count of the orbital expression for a model
without generating it. (#144)step_dummy() and step_indicate_na() now
generate SQL compatible with Snowflake and other databases that don’t
support casting booleans directly to numeric types. (#145)orbital() now works with
boost_tree(engine = "catboost") models for numeric, class,
and probability predictions. (#90)
orbital() now works with
boost_tree(engine = "lightgbm") models for numeric, class,
and probability predictions. (#89)
orbital() now works with
decision_tree(engine = "rpart") models for numeric, class,
and probability predictions. (#128)
orbital() now works with
mars(engine = "earth") models for class and probability
predictions. (#127)
orbital() now works with
multinom_reg(engine = "glmnet") models for class and
probability predictions. (#127)
orbital() now works with
rand_forest(engine = "randomForest") models for class and
probability predictions. (#127)
orbital() now works with
rand_forest(engine = "ranger") models for class and
probability predictions. (#127)
orbital() gains a separate_trees
argument for tree ensemble models (xgboost, lightgbm, catboost, ranger,
randomForest). When TRUE, each tree is emitted as a
separate intermediate column before being summed, which can enable
parallel evaluation in columnar databases like DuckDB, Snowflake, and
BigQuery. For models with many trees, the final summation is
automatically batched in groups of 50 to avoid expression depth limits
in databases. See the “Separate trees” vignette for details.
(#105)
Added support for step_spline_b(),
step_spline_convex(), step_spline_monotone(),
step_spline_natural(), and
step_spline_nonnegative() from the recipes package.
(#99)
step_YeoJohnson() is now supported. (#96)
Binary classification probability predictions now generate
cleaner code by having the second probability reference the first (e.g.,
.pred_1 = 1 - .pred_0) instead of duplicating the full
expression. (#100)
New “Database deployment” vignette shows how to deploy predictions to a database as tables or views. (#74)
New “SQL size” vignette documents how model type and hyperparameters affect generated SQL size, and shows how to jointly tune for predictive performance and SQL complexity.
Added support for tailor package and its integration into
workflows. The following adjustments have gained orbital()
support. (#103)
adjust_equivocal_zone()adjust_numeric_range()adjust_predictions_custom()adjust_probability_threshold()Added show_query() method for orbital objects.
(#106)
Fixed printing bug where output would get malformed if coefficients had similarities. (#115)
Fixed bug where PCA steps didn’t work if they were trained with more than 99 predictors. (#82)
step_pca_sparse() no longer generate code with terms
with 0 in them. (#51)
Fixed bugs in all PCA steps where an error occurred depending on which predictors were selected. (#52)
Fixed bug where large PCA results wouldn’t work with data bases. (#84)
orbital() has gained type argument to
change prediction type. (#66)
orbital() now works with
logistic_reg(engine = "glm") models for class prediction
and probability predictions. (#62, #66)
orbital() now works with
boost_tree(engine = "xgboost") models for class prediction
and probability predictions. (#71)
orbital() now works with
decision_tree(engine = "partykit") models for class
prediction and probability predictions. (#77)
augment() method for orbital() object
have been added. (#55)
orbital() gained prefix argument to
allow for renaming of prediction columns. (#59)
Support for step_dummy(),
step_impute_mean(), step_impute_median(),
step_impute_mode(), step_unknown(),
step_novel(), step_other(),
step_BoxCox(), step_inverse(),
step_mutate(), step_sqrt(),
step_indicate_na(), step_range(),
step_intercept(), step_ratio(),
step_lag(), step_log(),
step_rename() has been added. (#17)
Support for step_upsample(),
step_smote(), step_smotenc(),
step_bsmote(), step_adasyn(),
step_rose(), step_downsample(),
step_nearmiss(), and step_tomek() has been
added. (#21)
Support for step_bin2factor(),
step_discretize(), step_lencode_mixed(),
step_lencode_glm(), step_lencode_bayes() has
been added. (#22)
Support for step_pca_sparse(),
step_pca_sparse_bayes() and
step_pca_truncated() as been added. (#23)
orbital() now works on tune::last_fit()
objects. (#13)
orbital_predict() has been removed and replaced with
the more idiomatic predict() method. (#10)