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_BoxCox()
  • step_adasyn()
  • step_bin2factor()
  • 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()