Man pages for parsnip
A Common API to Modeling and Analysis Functions

add_on_exportsFunctions required for parsnip-adjacent packages
add_rowindexAdd a column of row numbers to a data frame
augmentAugment data with predictions
boost_treeBoosted trees
C5.0_trainBoosted trees via C5.0
check_empty_ellipseCheck to ensure that ellipses are empty
control_parsnipControl the fit function
contr_one_hotContrast function for one-hot encodings
convert_helpersHelper functions to convert between formula and matrix...
convert_stan_intervalConvenience function for intervals
decision_treeDecision trees
descriptorsData Set Characteristics Available when Fitting Models
details_boost_tree_C5.0Boosted trees via C5.0
details_boost_tree_sparkBoosted trees via Spark
details_boost_tree_xgboostBoosted trees via xgboost
details_decision_tree_C5.0Decision trees via C5.0
details_decision_tree_rpartDecision trees via CART
details_decision_tree_sparkDecision trees via Spark
details_gen_additive_mod_mgcvGeneralized additive models via mgcv
details_linear_reg_glmnetLinear regression via glmnet
details_linear_reg_kerasLinear regression via keras/tensorflow
details_linear_reg_lmLinear regression via lm
details_linear_reg_sparkLinear regression via spark
details_linear_reg_stanLinear regression via Bayesian Methods
details_logistic_reg_glmLogistic regression via glm
details_logistic_reg_glmnetLogistic regression via glmnet
details_logistic_reg_kerasLogistic regression via keras
details_logistic_reg_LiblineaRLogistic regression via LiblineaR
details_logistic_reg_sparkLogistic regression via spark
details_logistic_reg_stanLogistic regression via stan
details_mars_earthMultivariate adaptive regression splines (MARS) via earth
details_mlp_kerasMultilayer perceptron via keras
details_mlp_nnetMultilayer perceptron via nnet
details_multinom_reg_glmnetMultinomial regression via glmnet
details_multinom_reg_kerasMultinomial regression via keras
details_multinom_reg_nnetMultinomial regression via nnet
details_multinom_reg_sparkMultinomial regression via spark
details_nearest_neighbor_kknnK-nearest neighbors via kknn
details_rand_forest_randomForestRandom forests via randomForest
details_rand_forest_rangerRandom forests via ranger
details_rand_forest_sparkRandom forests via spark
details_svm_linear_kernlabLinear support vector machines (SVMs) via kernlab
details_svm_linear_LiblineaRLinear support vector machines (SVMs) via LiblineaR
details_svm_poly_kernlabPolynomial support vector machines (SVMs) via kernlab
details_svm_rbf_kernlabRadial basis function support vector machines (SVMs) via...
doc-toolsTools for dynamically documenting packages
eval_argsEvaluate parsnip model arguments
extract-parsnipExtract elements of a parsnip model object
fitFit a Model Specification to a Dataset
gen_additive_modGeneralized additive models (GAMs)
get_model_envWorking with the parsnip model environment
glance.model_fitConstruct a single row summary "glance" of a model, fit, or...
glmnet-detailsTechnical aspects of the glmnet model
glmnet_helpersHelper functions for checking the penalty of glmnet models
glmnet_helpers_predictionOrganize glmnet predictions
has_multi_predictTools for models that predict on sub-models
keras_mlpSimple interface to MLP models via keras
linear_regLinear regression
logistic_regLogistic regression
make_callMake a parsnip call expression
make_classesPrepend a new class
marsMultivariate adaptive regression splines (MARS)
maybe_matrixFuzzy conversions
min_colsExecution-time data dimension checks
mlpSingle layer neural network
model_dbparsnip model specification database
model_fitModel Fit Object Information
model_printerPrint helper for model objects
model_specModel Specification Information
multinom_regMultinomial regression
multi_predictModel predictions across many sub-models
nearest_neighborK-nearest neighbors
nullmodelFit a simple, non-informative model
null_modelNull model
other_predictOther predict methods.
parsnip_addinStart an RStudio Addin that can write model specifications
parsnip_updateUpdate a model specification
predict.model_fitModel predictions
prepare_dataPrepare data based on parsnip encoding information
proportional_hazardsProportional hazards regression
rand_forestRandom forest
reexportsObjects exported from other packages
repair_callRepair a model call object
req_pkgsDetermine required packages for a model
rpart_trainDecision trees via rpart
set_argsChange elements of a model specification
set_engineDeclare a computational engine and specific arguments
set_new_modelTools to Register Models
show_callPrint the model call
show_enginesDisplay currently available engines for a model
stan_conf_intWrapper for stan confidence intervals
survival_regParametric survival regression
surv_regParametric survival regression
svm_linearLinear support vector machines
svm_polyPolynomial support vector machines
svm_rbfRadial basis function support vector machines
tidy._elnettidy methods for glmnet models
tidy._LiblineaRtidy methods for LiblineaR models
tidy.model_fitTurn a parsnip model object into a tidy tibble
tidy.nullmodelTidy method for null models
translateResolve a Model Specification for a Computational Engine
type_sum.model_specSuccinct summary of parsnip object
varyingA placeholder function for argument values
varying_argsDetermine varying arguments
xgb_trainBoosted trees via xgboost
parsnip documentation built on July 21, 2021, 5:08 p.m.