Supported models • tidypredict

This is the list of models tidypredict can parse: 43 fitted model classes from 30 modeling packages.

tidypredict dispatches on the class of the fitted model, so a model is supported if the “Fit with” column covers how it was fitted. Models fitted through parsnip are unwrapped and handed to the same code, so the “parsnip” column is not a separate list of capabilities: it names the spec and engine that produce each fitted class. A blank there means tidypredict has no test for that route, not that it is known to fail.

Regression

Model	Fit with	parsnip	Details
Linear regression	`stats::lm()`	`linear_reg(engine = "lm")`	article
Generalized linear regression	`stats::glm()`	`linear_reg(engine = "glm")`, `logistic_reg(engine = "glm")`	article
Regularized regression	`glmnet::glmnet()`	`linear_reg()`, `logistic_reg()`, `multinom_reg()` with `engine = "glmnet"`	article
Regularized linear models	`LiblineaR::LiblineaR()`	`logistic_reg(engine = "LiblineaR")`, `svm_linear(engine = "LiblineaR")`
Quantile regression	`quantreg::rq()`, `quantreg::rqs()`	`linear_reg(engine = "quantreg")`
Multinomial regression	`nnet::multinom()`	`multinom_reg(engine = "nnet")`	article
Support vector machine	`kernlab::ksvm()`	`svm_linear(engine = "kernlab")`
Neural network	`nnet::nnet()`	`mlp(engine = "nnet")`	article
MARS	`earth::earth()`	`mars(engine = "earth")`	article
Partial least squares	`mixOmics::pls()`, `spls()`, `plsda()`, `splsda()`	`pls(engine = "mixOmics")`	article
Null model	`parsnip::nullmodel()`	`null_model()`	article

Classification and discriminant analysis

Model	Fit with	parsnip	Details
Naive Bayes	`naivebayes::naive_bayes()`, `klaR::NaiveBayes()`	`naive_Bayes()` with `engine = "naivebayes"` or `"klaR"`	article
Linear discriminant analysis	`MASS::lda()`	`discrim_linear(engine = "MASS")`	article
Quadratic discriminant analysis	`MASS::qda()`	`discrim_quad(engine = "MASS")`	article
Flexible discriminant analysis	`mda::fda()`	`discrim_linear(engine = "mda")`	article
Shrinkage discriminant analysis	`sda::sda()`	`discrim_linear(engine = "sda")`	article
Regularized discriminant analysis	`sparsediscrim::lda_diag()`, `lda_shrink_mean()`, `lda_shrink_cov()`, `lda_emp_bayes_eigen()`	`discrim_linear(engine = "sparsediscrim")`	article

Trees and forests

Model	Fit with	parsnip	Details
Decision tree	`rpart::rpart()`	`decision_tree(engine = "rpart")`	article
Decision tree	`C50::C5.0()`	`decision_tree(engine = "C5.0")`, `C5_rules(engine = "C5.0")`	article
Conditional inference tree	`partykit::ctree()`		article
Random forest	`randomForest::randomForest()`	`rand_forest(engine = "randomForest")`	article
Random forest	`ranger::ranger()`	`rand_forest(engine = "ranger")`	article
Conditional inference forest	`partykit::cforest()`	`rand_forest(engine = "partykit")`	article
Oblique random forest	`aorsf::orsf()`	`rand_forest(engine = "aorsf")`	article
Bagged trees	`baguette::bagger()`	`bag_tree()` with `engine = "rpart"` or `"C5.0"`	article
BART	`dbarts::bart()`	`bart(engine = "dbarts")`	article

Boosting and rules

Model	Fit with	parsnip	Details
XGBoost	`xgboost::xgb.train()`	`boost_tree(engine = "xgboost")`	article
LightGBM	`lightgbm::lgb.train()`	`boost_tree(engine = "lightgbm")`, via bonsai	article
CatBoost	`catboost::catboost.train()`	`boost_tree(engine = "catboost")`, via bonsai	article
Boosted C5.0 trees	`C50::C5.0()` with `trials`	`boost_tree(engine = "C5.0")`	article
Model-based boosting	`mboost::blackboost()`
Cubist	`Cubist::cubist()`	`cubist_rules(engine = "Cubist")`	article
RuleFit	`xrf::xrf()`	`rule_fit(engine = "xrf")`
H2O gradient boosting	`h2o::h2o.gbm()`	`boost_tree(engine = "h2o_gbm")`, via agua	article
H2O RuleFit	`h2o::h2o.rulefit()`	`rule_fit(engine = "h2o")`, via agua	article

Intervals

tidypredict_interval() and tidypredict_sql_interval() are narrower than tidypredict_fit(): they only support lm() and glm() models.

Adding a model

If a model you need is missing, open an issue. CONTRIBUTING.md describes what a new model needs, and the non-R models article covers the other direction: writing a parsed model spec by hand so a model fitted outside R can be used here.