.extract_surv_status Extract survival status .extract_surv_time Extract survival time .model_param_name_key Translate names of model tuning parameters C5_rules C5.0 rule-based classification models add_rowindex Add a column of row numbers to a data frame augment.model_fit Augment data with predictions auto_ml Automatic Machine Learning autoplot.model_fit Create a ggplot for a model object bag_mars Ensembles of MARS models bag_mlp Ensembles of neural networks bag_tree Ensembles of decision trees bart Bayesian additive regression trees (BART) boost_tree Boosted trees case_weights Using case weights with parsnip case_weights_allowed Determine if case weights are used contr_one_hot Contrast function for one-hot encodings control_parsnip Control the fit function ctree_train A wrapper function for conditional inference tree models cubist_rules Cubist rule-based regression models decision_tree Decision trees descriptors Data Set Characteristics Available when Fitting Models discrim_flexible Flexible discriminant analysis discrim_linear Linear discriminant analysis discrim_quad Quadratic discriminant analysis discrim_regularized Regularized discriminant analysis extract-parsnip Extract elements of a parsnip model object fit.model_spec Fit a Model Specification to a Dataset gen_additive_mod Generalized additive models (GAMs) glance.model_fit Construct a single row summary "glance" of a model, fit, or other object glm_grouped Fit a grouped binomial outcome from a data set with case weights linear_reg Linear regression logistic_reg Logistic regression mars Multivariate adaptive regression splines (MARS) max_mtry_formula 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. maybe_matrix Fuzzy conversions min_cols Execution-time data dimension checks mlp Single layer neural network model_fit Model Fit Object Information model_formula Formulas with special terms in tidymodels model_spec Model Specification Information multi_predict Model predictions across many sub-models multinom_reg Multinomial regression naive_Bayes Naive Bayes models nearest_neighbor K-nearest neighbors null_model Null model parsnip_addin Start an RStudio Addin that can write model specifications pls Partial least squares (PLS) poisson_reg Poisson regression models rand_forest Random forest repair_call Repair a model call object req_pkgs Determine required packages for a model required_pkgs.model_spec Determine required packages for a model rule_fit RuleFit models set_args Change elements of a model specification set_engine Declare a computational engine and specific arguments show_engines Display currently available engines for a model svm_linear Linear support vector machines svm_poly Polynomial support vector machines svm_rbf Radial basis function support vector machines tidy.model_fit Turn a parsnip model object into a tidy tibble translate Resolve a Model Specification for a Computational Engine update.bag_mars Updating a model specification