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Changelog

Source: NEWS.md

tidypredict (development version)

New Model Supports

  • Added support for CatBoost models (catboost.Model). Supports regression, binary classification, and multiclass classification objectives including RMSE, MAE, Quantile, MAPE, Poisson, Logloss, CrossEntropy, MultiClass, and MultiClassOneVsAll. Categorical features are handled automatically for parsnip/bonsai models; for raw CatBoost models use set_catboost_categories(). (#TBD)

  • Added support for LightGBM models (lgb.Booster). Supports regression, binary classification, multiclass classification, and categorical features. (#TBD)

tidypredict 1.0.1

CRAN release: 2025-12-13

Bug Fixes

  • Fixed bug where base_score wasn’t extracted correctly xgboost for version 3 or higher. (#173)

tidypredict 1.0.0

CRAN release: 2025-11-29

Breaking Changes

  • Random forest implementations (ranger and randomForest) will now produce a single formula instead of a list of expressions. (#84)

New Model Supports

  • Added support for glmnet models. (#165)

Improvements

  • xgboost models with objectives "reg:tweedie" and "count:poisson" are now supported. (#72, @SimonCoulombe)

  • tree based models now uses .default argument in produced case_when() code when applicable. (#153)

  • Speed up tidypredict_fit() for partykit and ranger packages. (#125)

  • Speed up tidypredict_fit() for xgboost models. (#130)

  • randomForest models now support regression outcomes. (#77)

  • An informative error will now be thrown if a lm model cannot be processed due to having linear combinations of predictors. (#124)

  • linear models such as lm() and glm() now work with interactions created with * and :. (#74)

  • Cubist rules will return simplified rules whenever possible to avoid multiplying by 0 and 1. (#152)

  • Make work with xgboost version > 2.0.0.0. (#169)

Bug Fixes

  • Fixed a bug where the intercept was added incorrectly to the result for cubist models. (#58)

  • Fixed bug where tidypredict would error on Cubist models without conditions. (#127)

  • Fixed bug where Cubst models incorrectly combined rules and committees. (#134)

tidypredict 0.5.1

CRAN release: 2024-12-19

  • Exported a number of internal functions to be used in {orbital} package

tidypredict 0.5

CRAN release: 2023-01-18

  • Changes maintainer to Edgar Ruiz

  • Updates author’s email addresses.

  • Removes dependency with stringr

  • Fixes issue with earth parsed_models (#108)

  • Addresses issues with XGBoost models

  • Improvements to XGBoosts tests

tidypredict 0.4.9

CRAN release: 2022-05-25

  • Fixes issue handling GLM Binomial earth models (#97)

  • Adds capability to handle single simple Cubist models (#57)

  • Fixed parenthesis issue in the creation of the interval formula (#76)

  • Fixed bug in SQL query generation for XGBoost models with objective binary:logistic.

  • Re-licensed package from GPL-3 to MIT. See consent from copyright holders here.

tidypredict 0.4.8

CRAN release: 2020-10-28

  • CRAN submission for a broken test case.

tidypredict 0.4.7

CRAN release: 2020-10-05

  • Change to with with version 5.1.2 and above of the earth package. As a result, tidypredict will only parse objects created by this and later versions of earth.

tidypredict 0.4.6

CRAN release: 2020-07-23

  • Small release for xgboost changes.

tidypredict 0.4.5

CRAN release: 2020-02-10

  • Switches maintainer to Max Kuhn

tidypredict 0.4.3

CRAN release: 2019-09-03

  • Adds support for categorical predictors in partykit

  • Fixes parsnip tests to meet standards of new CRAN version

tidypredict 0.4.2

CRAN release: 2019-07-15

  • Simplifies tests that verify ranger

  • Adds fit method for parsed xgboost models

  • Sets conditional requirement for xgboost, for test and vignette

tidypredict 0.4.0

CRAN release: 2019-07-12

New features

  • Parses ranger classification models.

  • Adds method support for broom’s tidy() function. Regression models only

  • Adds as_parsed_model() function. It adds the proper class components to the list.

  • Adds initial support for partykit’s ctree() model

  • Adds support for parsnip fitted models: lm, randomForest, ranger, and earth

  • Adds support for xgb.Booster models provided by the xgboost package (@Athospd, #43)

  • Adds support for Cubist::cubist() models (# 36)

tidypredict 0.3.0

CRAN release: 2019-01-10

New features

  • Adds support for MARS models provided by the earth package

Improvements

  • New parsed models are now list objects as opposed to data frames.

  • tidypredict_to_column() no longer supports ranger and randomForest because of the multiple queries generated by multiple trees.

  • All functions that read the parsed models and create the tidy eval formula now use the list object.

  • Most of the code that depends on dplyr programming has been removed.

  • Removes dependencies on: tidyr, tibble

  • The x/y interface for earth models can now be used.

Bug Fixes

  • It now returns all of the trees instead of just one for tree based models (randomForest & ranger) (#29)

tidypredict 0.2.1

CRAN release: 2018-12-20

Bug Fixes

  • tibble 2.0.0 compatibility fix (@krlmlr)

tidypredict 0.2.0

CRAN release: 2018-02-25

New features

Bug fixes