Package: parsnip 1.6.0.9000

Max Kuhn

parsnip: A Common API to Modeling and Analysis Functions

A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', 'H2O', etc).

Authors:Max Kuhn [cre, aut], Davis Vaughan [aut], Emil Hvitfeldt [ctb], Posit Software, PBC [cph, fnd]

parsnip_1.6.0.9000.tar.gz
parsnip_1.6.0.9000.zip(r-4.7)parsnip_1.6.0.9000.zip(r-4.6)parsnip_1.6.0.9000.zip(r-4.5)
parsnip_1.6.0.9000.tgz(r-4.6-any)parsnip_1.6.0.9000.tgz(r-4.5-any)
parsnip_1.6.0.9000.tar.gz(r-4.7-any)parsnip_1.6.0.9000.tar.gz(r-4.6-any)
parsnip_1.6.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
parsnip/json (API)

# Install 'parsnip' in R:

install.packages('parsnip', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

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

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

On CRAN:

16.56 score 654 stars 85 packages 3.7k scripts 44k downloads 2 mentions 193 exports 34 dependencies

Last updated from:5b50c54cbb. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	235
source / vignettes	OK	281
linux-release-x86_64	OK	243
macos-release-arm64	OK	166
macos-oldrel-arm64	OK	165
windows-devel	OK	277
windows-release	OK	273
windows-oldrel	OK	267
wasm-release	OK	148

Exports:.censoring_weights_graf .check_glmnet_penalty_fit .check_glmnet_penalty_predict .cols .convert_form_to_xy_fit .convert_form_to_xy_new .convert_xy_to_form_fit .convert_xy_to_form_new .dat .extract_surv_status .extract_surv_time .facts .get_prediction_column_names .lvls .model_param_name_key .obs .organize_glmnet_pred .preds .x .y %>%add_rowindex augment auto_ml autoplot bag_mars bag_mlp bag_tree bart boost_tree C5_rules C5.0_train case_weights_allowed cforest_train check_args check_empty_ellipse check_final_param condense_control contr_one_hot control_parsnip convert_stan_interval ctree_train cubist_rules dbart_predict_calc decision_tree discrim_flexible discrim_linear discrim_quad discrim_regularized ensure_parsnip_format eval_args extract_fit_engine extract_fit_time extract_parameter_dials extract_parameter_set_dials extract_spec_parsnip find_engine_files fit fit_control fit_xy fit_xy.model_spec fit.model_spec format_class format_classprobs format_hazard format_linear_pred format_num format_predictions format_survival format_time frequency_weights gen_additive_mod get_dependency get_encoding get_fit get_from_env get_model_env get_pred_type glance glm_grouped has_multi_predict importance_weights is_varying keras_activations keras_mlp keras_predict_classes keras3_activations keras3_mlp keras3_predict_classes knit_engine_docs linear_reg list_md_problems logistic_reg make_call make_classes make_engine_list make_seealso_list mars matrix_to_quantile_pred max_mtry_formula maybe_data_frame maybe_matrix mcqrnn_train min_cols min_rows mlp model_arg_exists model_printer multi_predict multi_predict_args multinom_reg naive_Bayes nearest_neighbor new_model_spec null_model null_value nullmodel ordinal_reg parsnip_addin pls poisson_reg pred_value_template predict_class predict_class.model_fit predict_classprob predict_classprob.model_fit predict_confint predict_confint.model_fit predict_hazard predict_hazard.model_fit predict_linear_pred predict_linear_pred.model_fit predict_numeric predict_numeric.model_fit predict_predint predict_predint.model_fit predict_quantile predict_quantile.model_fit predict_raw predict_raw.model_fit predict_survival predict_survival.model_fit predict_time predict_time.model_fit predict.model_fit prepare_data print_model_spec prompt_missing_implementation proportional_hazards rand_forest repair_call req_pkgs required_pkgs rule_fit set_args set_dependency set_encoding set_engine set_env_val set_fit set_in_env set_mode set_model_arg set_model_engine set_model_mode set_new_model set_pred set_tf_seed show_call show_engines show_fit show_model_info spec_is_loaded spec_is_possible stan_conf_int surv_reg survival_reg svm_linear svm_poly svm_rbf tidy translate translate.default tune update_dot_check update_engine_parameters update_main_parameters update_model_info_file update_spec varying varying_args xgb_predict xgb_train

Dependencies:cli codetools cpp11 dplyr farver generics ggplot2 globals glue gtable hardhat isoband labeling lifecycle magrittr pillar pkgconfig prettyunits purrr R6 RColorBrewer rlang S7 scales sparsevctrs stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

Introduction to parsnip

Motivation | Placeholders for Parameters | Specifying Arguments | Process

Rendered fromparsnip.Rmdusingknitr::rmarkdown

Last update: 2025-12-01
Started: 2021-11-05

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Extract survival status	.extract_surv_status
Extract survival time	.extract_surv_time
Obtain names of prediction columns for a fitted model or workflow	.get_prediction_column_names
Add a column of row numbers to a data frame	add_rowindex
Augment data with predictions	augment.model_fit
Automatic Machine Learning	auto_ml
Create a ggplot for a model object	autoplot.glmnet autoplot.model_fit
Ensembles of MARS models	bag_mars
Ensembles of neural networks	bag_mlp
Ensembles of decision trees	bag_tree
Bayesian additive regression trees (BART)	bart
Boosted trees	boost_tree
C5.0 rule-based classification models	C5_rules
Using case weights with parsnip	case_weights
Determine if case weights are used	case_weights_allowed
Control the fit function	control_parsnip
A wrapper function for conditional inference tree models	cforest_train ctree_train
Cubist rule-based regression models	cubist_rules
Decision trees	decision_tree
Data Set Characteristics Available when Fitting Models	.cols .dat .facts .lvls .obs .preds .x .y descriptors
Flexible discriminant analysis	discrim_flexible
Linear discriminant analysis	discrim_linear
Quadratic discriminant analysis	discrim_quad
Regularized discriminant analysis	discrim_regularized
Extract elements of a parsnip model object	extract-parsnip extract_fit_engine.model_fit extract_fit_time.model_fit extract_parameter_dials.model_spec extract_parameter_set_dials.model_spec extract_spec_parsnip.model_fit
Fit a Model Specification to a Dataset	fit.model_spec fit_xy.model_spec
Generalized additive models (GAMs)	gen_additive_mod
Construct a single row summary "glance" of a model, fit, or other object	glance.model_fit
Fit a grouped binomial outcome from a data set with case weights	glm_grouped
Linear regression	linear_reg
Logistic regression	logistic_reg
Multivariate adaptive regression splines (MARS)	mars
Determine largest value of mtry from formula. This function potentially caps the value of 'mtry' based on a formula and data set. This is a safe approach for survival and/or multivariate models.	max_mtry_formula
Fuzzy conversions	maybe_data_frame maybe_matrix
Execution-time data dimension checks	min_cols min_rows
Single layer neural network	mlp
Model Fit Objects	model_fit
Formulas with special terms in tidymodels	model_formula
Model Specifications	model_spec
Model predictions across many sub-models	multi_predict multi_predict.default multi_predict._C5.0 multi_predict._earth multi_predict._elnet multi_predict._glmnetfit multi_predict._lognet multi_predict._multnet multi_predict._torch_mlp multi_predict._train.kknn multi_predict._xgb.Booster
Multinomial regression	multinom_reg
Naive Bayes models	naive_Bayes
K-nearest neighbors	nearest_neighbor
Null model	null_model
Start an RStudio Addin that can write model specifications	parsnip_addin
Partial least squares (PLS)	pls
Poisson regression models	poisson_reg
Random forest	rand_forest
Repair a model call object	repair_call
Determine required packages for a model	req_pkgs
Determine required packages for a model	required_pkgs.model_fit required_pkgs.model_spec
RuleFit models	rule_fit
Change elements of a model specification	set_args set_mode set_mode.model_spec
Declare a computational engine and specific arguments	set_engine
Display currently available engines for a model	show_engines
Using sparse data with parsnip	sparse_data
Linear support vector machines	svm_linear
Polynomial support vector machines	svm_poly
Radial basis function support vector machines	svm_rbf
Turn a parsnip model object into a tidy tibble	tidy.model_fit
Resolve a Model Specification for a Computational Engine	translate translate.default
Updating a model specification	parsnip_update update.bag_mars update.bag_mlp update.bag_tree update.bart update.boost_tree update.C5_rules update.cubist_rules update.decision_tree update.discrim_flexible update.discrim_linear update.discrim_quad update.discrim_regularized update.gen_additive_mod update.linear_reg update.logistic_reg update.mars update.mlp update.multinom_reg update.naive_Bayes update.nearest_neighbor update.ordinal_reg update.pls update.poisson_reg update.proportional_hazards update.rand_forest update.rule_fit update.survival_reg update.surv_reg update.svm_linear update.svm_poly update.svm_rbf