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

boost_treeGeneral Interface for Boosted Trees
C5.0_trainBoosted trees via C5.0
check_empty_ellipseCheck to ensure that ellipses are empty
decision_treeGeneral Interface for Decision Tree Models
descriptorsData Set Characteristics Available when Fitting Models
fitFit a Model Specification to a Dataset
fit_controlControl the fit function
keras_mlpSimple interface to MLP models via keras
lending_clubLoan Data
linear_regGeneral Interface for Linear Regression Models
logistic_regGeneral Interface for Logistic Regression Models
make_classesPrepend a new class
marsGeneral Interface for MARS
mlpGeneral Interface for Single Layer Neural Network
model_fitModel Fit Object Information
model_printerPrint helper for model objects
model_specModel Specification Information
multinom_regGeneral Interface for Multinomial Regression Models
multi_predictModel predictions across many sub-models
nearest_neighborGeneral Interface for K-Nearest Neighbor Models
other_predictOther predict methods.
predict.model_fitModel predictions
rand_forestGeneral Interface for Random Forest Models
reexportsObjects exported from other packages
rpart_trainDecision trees via rpart
set_argsChange elements of a model specification
set_engineDeclare a computational engine and specific arguments
show_callPrint the model call
surv_regGeneral Interface for Parametric Survival Models
svm_polyGeneral interface for polynomial support vector machines
svm_rbfGeneral interface for radial basis function support vector...
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
wa_churnWatson Churn Data
xgb_trainBoosted trees via xgboost
tidymodels/parsnip documentation built on Nov. 7, 2018, 12:52 a.m.