Package: tidypredict 1.1.0.9000

Emil Hvitfeldt

tidypredict: Run Predictions Inside the Database

It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), rpart(), earth(), xgb.Booster.complete(), lgb.Booster(), catboost.Model(), cubist(), and ctree() models.

Authors:Emil Hvitfeldt [aut, cre], Edgar Ruiz [aut], Max Kuhn [aut]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tidypredict/json (API)

# Install 'tidypredict' in R:
install.packages('tidypredict', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidymodels/tidypredict/issues

Pkgdown/docs site:https://tidypredict.tidymodels.org

On CRAN:

Conda:

dbplyrdplyrpurrrrlang

11.98 score 264 stars 2 packages 234 scripts 1.4k downloads 27 exports 26 dependencies

Last updated from:5d33183137. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK195
source / vignettesOK237
linux-release-x86_64OK172
macos-release-arm64OK116
macos-oldrel-arm64OK131
windows-develOK124
windows-releaseOK116
windows-oldrelOK125
wasm-releaseOK132

Exports:.build_case_when_tree.build_linear_pred.build_nested_case_when_tree.extract_catboost_trees.extract_earth_multiclass.extract_glmnet_multiclass.extract_lgb_trees.extract_partykit_classprob.extract_ranger_classprob.extract_ranger_trees.extract_rf_classprob.extract_rf_trees.extract_rpart_classprob.extract_xgb_trees.partykit_tree_info_full.rpart_tree_info_fullacceptable_formulaas_parsed_modelparse_modelset_catboost_categoriestidytidypredict_fittidypredict_intervaltidypredict_sqltidypredict_sql_intervaltidypredict_testtidypredict_to_column

Dependencies:clicpp11dplyrevaluategenericsgluehighrjsonliteknitrlifecyclemagrittrpillarpkgconfigpurrrR6rlangstringistringrtibbletidyrtidyselectutf8vctrswithrxfunyaml

catboost models
tidypredict_ functions | Supported objectives | Regression objectives (identity transform) | Binary classification (sigmoid transform) | Multiclass classification | Binary classification example | Multiclass classification example | Categorical features | With parsnip/bonsai (recommended) | With raw CatBoost | Parse model spec | Limitations
Rendered fromcatboost.Rmdusingknitr::rmarkdown

Last update: 2026-02-25
Started: 2026-02-12

Cubist models
tidypredict_ functions | Parse model spec | Limitations
Rendered fromcubist.Rmdusingknitr::rmarkdown

Last update: 2026-02-25
Started: 2019-07-07

Float precision at split boundaries
The issue | Which models are affected? | Example | What tidypredict does | Pros and cons | Recommendations
Rendered fromfloat-precision.Rmdusingknitr::rmarkdown

Last update: 2026-02-25
Started: 2026-02-25

LightGBM models
tidypredict_ functions | Supported objectives | Regression objectives (identity transform) | Regression objectives (exp transform) | Binary classification (sigmoid transform) | Multiclass classification | Binary classification example | Categorical features | parsnip | Parse model spec | Limitations
Rendered fromlightgbm.Rmdusingknitr::rmarkdown

Last update: 2026-02-25
Started: 2026-02-11

XGBoost models
tidypredict_ functions | parsnip | Parse model spec
Rendered fromxgboost.Rmdusingknitr::rmarkdown

Last update: 2026-02-25
Started: 2019-07-07

How tidypredict generates tree formulas
Nested vs flat case_when | Flat case_when (old approach) | Nested case_when (current approach) | Why nested is better | Parsed model versions
Rendered fromtree-internals.Rmdusingknitr::rmarkdown

Last update: 2026-02-20
Started: 2026-02-20

Decision trees, using rpart
How it works | Under the hood | Classification | parsnip | Categorical predictors | Surrogate splits
Rendered fromrpart.Rmdusingknitr::rmarkdown

Last update: 2026-02-20
Started: 2026-02-20

Random Forest
How it works | Under the hood | parsnip
Rendered fromrf.Rmdusingknitr::rmarkdown

Last update: 2025-11-26
Started: 2019-07-03

Random Forest, using Ranger
How it works | Under the hood | parsnip
Rendered fromranger.Rmdusingknitr::rmarkdown

Last update: 2025-11-26
Started: 2018-02-20

glmnet models
tidypredict_ functions | parsnip | Parse model spec
Rendered fromglmnet.Rmdusingknitr::rmarkdown

Last update: 2025-11-07
Started: 2025-11-07

Linear Regression
Highlights & Limitations | How it works | Prediction intervals | Under the hood | How it performs | parsnip
Rendered fromlm.Rmdusingknitr::rmarkdown

Last update: 2025-11-04
Started: 2017-12-27

Database write-back
Example setup | Model preparation | Scenario 1 - Update scores | Scenario 2- Append new scores
Rendered fromsql.Rmdusingknitr::rmarkdown

Last update: 2023-10-31
Started: 2018-01-02

Generalized Linear Regression
Highlights & Limitations | How it works | Under the hood | How it performs
Rendered fromglm.Rmdusingknitr::rmarkdown

Last update: 2023-10-31
Started: 2017-12-27

MARS models via the earth package
tidypredict_ functions | GLM models | parsnip | Parse model spec
Rendered frommars.Rmdusingknitr::rmarkdown

Last update: 2023-10-31
Started: 2019-07-07

Create a regression spec - version 2
Rendered fromregression.Rmdusingknitr::rmarkdown

Last update: 2022-05-31
Started: 2019-07-06

Create a tree spec - version 2
Rendered fromtree.Rmdusingknitr::rmarkdown

Last update: 2022-05-31
Started: 2019-07-06

Non-R Models
python example | Read in R | tidypredict | broom
Rendered fromnon-r.Rmdusingknitr::rmarkdown

Last update: 2022-05-31
Started: 2019-07-12

Save and re-load models
Parse model | Saving the model | Re-load the model | broom
Rendered fromsave.Rmdusingknitr::rmarkdown

Last update: 2022-05-31
Started: 2017-12-30