A Common API to Modeling and Analysis Functions


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Documentation for package ‘parsnip’ version 0.1.7

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.cols Data Set Characteristics Available when Fitting Models
.dat Data Set Characteristics Available when Fitting Models
.facts Data Set Characteristics Available when Fitting Models
.lvls Data Set Characteristics Available when Fitting Models
.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
add_rowindex Add a column of row numbers to a data frame
augment.model_fit Augment data with predictions
boost_tree Boosted trees
control_parsnip Control the fit function
contr_one_hot Contrast function for one-hot encodings
decision_tree Decision trees
descriptors Data Set Characteristics Available when Fitting Models
extract-parsnip Extract elements of a parsnip model object
extract_fit_engine.model_fit Extract elements of a parsnip model object
extract_spec_parsnip.model_fit Extract elements of a parsnip model object
fit.model_spec Fit a Model Specification to a Dataset
fit_control Control the fit function
fit_xy.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
linear_reg Linear regression
logistic_reg Logistic regression
mars Multivariate adaptive regression splines (MARS)
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_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._lognet Model predictions across many sub-models
multi_predict._multnet 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
nearest_neighbor K-nearest neighbors
null_model Null model
parsnip_addin Start an RStudio Addin that can write model specifications
parsnip_update Update a model specification
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
req_pkgs.model_fit Determine required packages for a model
req_pkgs.model_spec Determine required packages for a model
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
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
update.boost_tree Update a model specification
update.decision_tree Update a model specification
update.gen_additive_mod Update a model specification
update.linear_reg Update a model specification
update.logistic_reg Update a model specification
update.mars Update a model specification
update.mlp Update a model specification
update.multinom_reg Update a model specification
update.nearest_neighbor Update a model specification
update.proportional_hazards Update a model specification
update.rand_forest Update a model specification
update.survival_reg Update a model specification
update.surv_reg Update a model specification
update.svm_linear Update a model specification
update.svm_poly Update a model specification
update.svm_rbf Update a model specification
varying_args.model_spec Determine varying arguments
varying_args.recipe Determine varying arguments
varying_args.step Determine varying arguments