Supported Models and recipes steps

The supported models currently all come from tidypredict right now.

Supported models

The following models are supported by tidypredict:

• Linear Regression - lm()
• Generalized Linear model - glm()
• Random Forest models - randomForest::randomForest()
• Random Forest models, via ranger - ranger::ranger()
• MARS models - earth::earth()
• XGBoost models - xgboost::xgb.Booster.complete()
• Cubist models - Cubist::cubist()
• Tree models, via partykit - partykit::ctree()

parsnip

tidypredict supports models fitted via the parsnip interface. The ones confirmed currently work in tidypredict are:

• lm() - parsnip: linear_reg() with “lm” as the engine.
• randomForest::randomForest() - parsnip: rand_forest() with “randomForest” as the engine.
• ranger::ranger() - parsnip: rand_forest() with “ranger” as the engine.
• earth::earth() - parsnip: mars() with “earth” as the engine.

Recipes steps

library(orbital)

The following 46 recipes steps are supported

• step_adasyn()
• step_bin2factor()
• step_BoxCox()
• step_bsmote()
• step_center()
• step_corr()
• step_discretize()
• step_downsample()
• step_dummy()
• step_filter_missing()
• step_impute_mean()
• step_impute_median()
• step_impute_mode()
• step_indicate_na()
• step_intercept()
• step_inverse()
• step_lag()
• step_lencode_bayes()
• step_lencode_glm()
• step_lencode_mixed()
• step_lincomb()
• step_log()
• step_mutate()
• step_nearmiss()
• step_normalize()
• step_novel()
• step_nzv()
• step_other()
• step_pca()
• step_pca_sparse()
• step_pca_sparse_bayes()
• step_pca_truncated()
• step_range()
• step_ratio()
• step_rename()
• step_rm()
• step_rose()
• step_scale()
• step_select()
• step_smote()
• step_smotenc()
• step_sqrt()
• step_tomek()
• step_unknown()
• step_upsample()
• step_zv()