A Common API to Modeling and Analysis Functions

Documentation for package ‘parsnip’ version 1.2.1

Help Pages

-- A --

add_rowindex	Add a column of row numbers to a data frame
augment.model_fit	Augment data with predictions
autoplot.glmnet	Create a ggplot for a model object
autoplot.model_fit	Create a ggplot for a model object
auto_ml	Automatic Machine Learning

-- B --

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

-- C --

C5_rules	C5.0 rule-based classification models
case_weights	Using case weights with parsnip
case_weights_allowed	Determine if case weights are used
cforest_train	A wrapper function for conditional inference tree models
control_parsnip	Control the fit function
contr_one_hot	Contrast function for one-hot encodings
ctree_train	A wrapper function for conditional inference tree models
cubist_rules	Cubist rule-based regression models

-- D --

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

-- E --

extract-parsnip	Extract elements of a parsnip model object
extract_fit_engine.model_fit	Extract elements of a parsnip model object
extract_parameter_dials.model_spec	Extract elements of a parsnip model object
extract_parameter_set_dials.model_spec	Extract elements of a parsnip model object
extract_spec_parsnip.model_fit	Extract elements of a parsnip model object

-- F --

fit.model_spec	Fit a Model Specification to a Dataset
fit_xy.model_spec	Fit a Model Specification to a Dataset

-- G --

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

-- L --

linear_reg	Linear regression
logistic_reg	Logistic regression

-- M --

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_data_frame	Fuzzy conversions
maybe_matrix	Fuzzy conversions
min_cols	Execution-time data dimension checks
min_rows	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
multinom_reg	Multinomial regression
multi_predict	Model predictions across many sub-models
multi_predict.default	Model predictions across many sub-models
multi_predict._C5.0	Model predictions across many sub-models
multi_predict._earth	Model predictions across many sub-models
multi_predict._elnet	Model predictions across many sub-models
multi_predict._glmnetfit	Model predictions across many sub-models
multi_predict._lognet	Model predictions across many sub-models
multi_predict._multnet	Model predictions across many sub-models
multi_predict._torch_mlp	Model predictions across many sub-models
multi_predict._train.kknn	Model predictions across many sub-models
multi_predict._xgb.Booster	Model predictions across many sub-models

-- N --

-- P --

parsnip_addin	Start an RStudio Addin that can write model specifications
parsnip_update	Updating a model specification
pls	Partial least squares (PLS)
poisson_reg	Poisson regression models

-- R --

rand_forest	Random forest
repair_call	Repair a model call object
required_pkgs.model_fit	Determine required packages for a model
required_pkgs.model_spec	Determine required packages for a model
req_pkgs	Determine required packages for a model
rule_fit	RuleFit models

-- S --

set_args	Change elements of a model specification
set_engine	Declare a computational engine and specific arguments
set_mode	Change elements of a model specification
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

-- T --

tidy.model_fit	Turn a parsnip model object into a tidy tibble
translate	Resolve a Model Specification for a Computational Engine
translate.default	Resolve a Model Specification for a Computational Engine

-- U --

update.bag_mars	Updating a model specification
update.bag_mlp	Updating a model specification
update.bag_tree	Updating a model specification
update.bart	Updating a model specification
update.boost_tree	Updating a model specification
update.C5_rules	Updating a model specification
update.cubist_rules	Updating a model specification
update.decision_tree	Updating a model specification
update.discrim_flexible	Updating a model specification
update.discrim_linear	Updating a model specification
update.discrim_quad	Updating a model specification
update.discrim_regularized	Updating a model specification
update.gen_additive_mod	Updating a model specification
update.linear_reg	Updating a model specification
update.logistic_reg	Updating a model specification
update.mars	Updating a model specification
update.mlp	Updating a model specification
update.multinom_reg	Updating a model specification
update.naive_Bayes	Updating a model specification
update.nearest_neighbor	Updating a model specification
update.pls	Updating a model specification
update.poisson_reg	Updating a model specification
update.proportional_hazards	Updating a model specification
update.rand_forest	Updating a model specification
update.rule_fit	Updating a model specification
update.survival_reg	Updating a model specification
update.surv_reg	Updating a model specification
update.svm_linear	Updating a model specification
update.svm_poly	Updating a model specification
update.svm_rbf	Updating a model specification

-- misc --

.cols	Data Set Characteristics Available when Fitting Models
.dat	Data Set Characteristics Available when Fitting Models
.extract_surv_status	Extract survival status
.extract_surv_time	Extract survival time
.facts	Data Set Characteristics Available when Fitting Models
.lvls	Data Set Characteristics Available when Fitting Models
.model_param_name_key	Translate names of model tuning parameters
.obs	Data Set Characteristics Available when Fitting Models
.preds	Data Set Characteristics Available when Fitting Models
.x	Data Set Characteristics Available when Fitting Models
.y	Data Set Characteristics Available when Fitting Models