tidypredict 1.1.0

New Model Supports

• Added support for rpart decision tree models (rpart). (#226)

• Added support for CatBoost models (catboost.Model). (#179, #187, #188)

  • Objectives: RMSE, MAE, Quantile, MAPE, Poisson, Huber, LogCosh, Expectile, Tweedie, Logloss, CrossEntropy, MultiClass, and MultiClassOneVsAll.
  • Tree types: oblivious (default SymmetricTree) and non-oblivious (Depthwise or Lossguide grow policy).
  • Categorical features are handled automatically for parsnip/bonsai models; for raw CatBoost models use set_catboost_categories().

• Added support for LightGBM models (lgb.Booster). (#177, #186)

  • Objectives: regression, binary classification, and multiclass classification.
  • Supports categorical features.
  • Supports linear trees (linear_tree = TRUE), which fit a linear model at each leaf instead of a constant.

Improvements

• Tree models (rpart, partykit, ranger, randomForest, xgboost, lightgbm, catboost) now generate nested case_when() expressions that mirror the tree structure, instead of flat expressions with all leaf conditions at the same level. This produces more efficient SQL and R code because conditions are evaluated hierarchically. (#227)

• parse_model() now documents the parsed model version system (v1/v2/v3) and model type classes in its help page. (#227)

• earth() models now support additional GLM families and link functions: Gamma, inverse.gaussian, probit, and cloglog. (#194, #195)

• glm() models now support additional families and link functions: Gamma family with inverse link, inverse.gaussian family with 1/mu^2 link, probit link, cloglog link, and sqrt link. (#203, #204, #205, #206, #207)

• glmnet() models now support Gamma family and Cox proportional hazards (family = "cox") models. (#200, #201)

• xgboost support now includes additional objectives: binary:hinge, reg:absoluteerror, reg:gamma, reg:pseudohubererror, and reg:squaredlogerror. (#184)

• Added a vignette on floating-point precision issues with tree-based models. (#231)

Bug Fixes

• tidypredict_fit() now correctly handles xgboost models with stump trees (single leaf, no splits). (#182)

• tidypredict_fit() now correctly handles xgboost DART booster models with rate_drop > 0. DART uses tree weight normalization during training, and these weights are now properly applied to each tree’s predictions. (#183)

• tidypredict_fit() now correctly incorporates base_score for xgboost models with count:poisson and reg:tweedie objectives. Previously, predictions were incorrect when base_score was not the default value. (#184)

• tidypredict_fit() now correctly averages tree predictions for LightGBM models with boosting="rf" instead of summing them. (#185)

• tidypredict_fit() now uses the correct split operator (<= instead of <) for ranger models. Previously, predictions were incorrect when data values exactly matched split values. (#189)

• tidypredict_fit() now correctly averages tree predictions for ranger models instead of summing them. Previously, predictions were num.trees times too large. (#190)

• tidypredict_fit() now throws a clear error for ranger and randomForest classification models, which are not supported. (#191, #193)

• tidypredict_fit() now uses the correct split operator (<= instead of <) for randomForest models. (#192)

• tidypredict_fit() now correctly handles partykit stump trees (models with no splits). (#196)

• tidypredict_fit() now works with glmnet() models that use family function syntax (e.g., family = gaussian()) instead of string syntax (e.g., family = "gaussian"). (#197)

• tidypredict_fit() now works with models that use family function syntax (e.g., family = gaussian()) instead of string syntax (e.g., family = "gaussian"). (#202)

tidypredict 1.0.1

Bug Fixes

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

tidypredict 1.0.0

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

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

tidypredict 0.5

• 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

• 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 submission for a broken test case.

tidypredict 0.4.7

• 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

• Small release for xgboost changes.

tidypredict 0.4.5

• Switches maintainer to Max Kuhn

tidypredict 0.4.3

• Adds support for categorical predictors in partykit

• Fixes parsnip tests to meet standards of new CRAN version

tidypredict 0.4.2

• 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

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

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

Bug Fixes

• tibble 2.0.0 compatibility fix (@krlmlr)

tidypredict 0.2.0

New features

• Add support for ranger() models.

Bug fixes

• Using x ~. in a randomForest() formula fails (#18 @washcycle).