Function reference • parsnip

Models

auto_ml(): Automatic Machine Learning

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

cubist_rules(): Cubist rule-based regression models

C5_rules(): C5.0 rule-based classification models

decision_tree(): Decision trees

discrim_flexible(): Flexible discriminant analysis

discrim_linear(): Linear discriminant analysis

discrim_quad(): Quadratic discriminant analysis

discrim_regularized(): Regularized discriminant analysis

gen_additive_mod(): Generalized additive models (GAMs)

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)

mlp(): Single layer neural network

multinom_reg(): Multinomial regression

naive_Bayes(): Naive Bayes models

nearest_neighbor(): K-nearest neighbors

null_model(): Null model

pls(): Partial least squares (PLS)

poisson_reg(): Poisson regression models

proportional_hazards(): Proportional hazards regression

rand_forest(): Random forest

rule_fit(): RuleFit models

survival_reg(): Parametric survival regression

svm_linear(): Linear support vector machines

svm_poly(): Polynomial support vector machines

svm_rbf(): Radial basis function support vector machines

Infrastructure

autoplot(<model_fit>) autoplot(<glmnet>): Create a ggplot for a model object

add_rowindex(): Add a column of row numbers to a data frame

augment(<model_fit>): Augment data with predictions

case_weights: Using case weights with parsnip

case_weights_allowed(): Determine if case weights are used

.cols() .preds() .obs() .lvls() .facts() .x() .y() .dat(): Data Set Characteristics Available when Fitting Models

extract_spec_parsnip(<model_fit>) extract_fit_engine(<model_fit>) extract_parameter_set_dials(<model_spec>) extract_parameter_dials(<model_spec>): Extract elements of a parsnip model object

fit(<model_spec>) fit_xy(<model_spec>): Fit a Model Specification to a Dataset

reexports autoplot %>% fit fit_xy tidy glance augment required_pkgs extract_spec_parsnip extract_fit_engine extract_parameter_set_dials extract_parameter_dials tune frequency_weights importance_weights varying_args: Objects exported from other packages

control_parsnip(): Control the fit function

glance(<model_fit>): Construct a single row summary "glance" of a model, fit, or other object

model_fit: Model Fit Object Information

model_formula: Formulas with special terms in tidymodels

model_spec: Model Specification Information

multi_predict multi_predict.default multi_predict._xgb.Booster multi_predict._C5.0 multi_predict._elnet multi_predict._lognet multi_predict._multnet multi_predict._glmnetfit multi_predict._earth multi_predict._torch_mlp multi_predict._train.kknn: Model predictions across many sub-models

parsnip_addin(): Start an RStudio Addin that can write model specifications

predict(<model_fit>) predict_raw(): Model predictions

repair_call(): Repair a model call object

set_args() set_mode(): Change elements of a model specification

set_engine(): Declare a computational engine and specific arguments

show_engines(): Display currently available engines for a model

tidy(<model_fit>): Turn a parsnip model object into a tidy tibble

translate(): Resolve a Model Specification for a Computational Engine

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(<pls>) update(<poisson_reg>) update(<proportional_hazards>) update(<rand_forest>) update(<rule_fit>) update(<surv_reg>) update(<survival_reg>) update(<svm_linear>) update(<svm_poly>) update(<svm_rbf>): Updating a model specification

ctree_train() cforest_train(): A wrapper function for conditional inference tree models

Developer tools

condense_control(): Condense control object into strictly smaller control object

contr_one_hot(): Contrast function for one-hot encodings

set_new_model() set_model_mode() set_model_engine() set_model_arg() set_dependency() get_dependency() set_fit() get_fit() set_pred() get_pred_type() show_model_info() pred_value_template() set_encoding() get_encoding(): Tools to Register Models

maybe_matrix() maybe_data_frame(): Fuzzy conversions

min_cols() min_rows(): Execution-time data dimension checks

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.

reexports autoplot %>% fit fit_xy tidy glance augment required_pkgs extract_spec_parsnip extract_fit_engine extract_parameter_set_dials extract_parameter_dials tune frequency_weights importance_weights varying_args: Objects exported from other packages

required_pkgs(<model_spec>) required_pkgs(<model_fit>): Determine required packages for a model

req_pkgs(): Determine required packages for a model

.extract_surv_status: Extract survival status

.extract_surv_time: Extract survival time

.model_param_name_key(): Translate names of model tuning parameters