# A Common API to Modeling and Analysis Functions

## Documentation for package ‘parsnip’ version 0.0.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

.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
boost_tree | General Interface for Boosted Trees
C5.0_train | Boosted trees via C5.0
check_times | Execution Time Data
decision_tree | General Interface for Decision Tree Models
descriptors | Data Set Characteristics Available when Fitting Models
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
keras_mlp | Simple interface to MLP models via keras
lending_club | Loan Data
linear_reg | General Interface for Linear Regression Models
logistic_reg | General Interface for Logistic Regression Models
mars | General Interface for MARS
mlp | General Interface for Single Layer Neural Network
model_fit | Model Fit Object Information
model_spec | Model Specification Information
multinom_reg | General Interface for Multinomial Regression Models
multi_predict | Model predictions across many sub-models
multi_predict.default | Model predictions across many sub-models
nearest_neighbor | General Interface for K-Nearest Neighbor Models
nullmodel | Fit a simple, non-informative model
nullmodel.default | Fit a simple, non-informative model
null_model | General Interface for null models
predict.model_fit | Model predictions
predict.nullmodel | Fit a simple, non-informative model
print.nullmodel | Fit a simple, non-informative model
rand_forest | General Interface for Random Forest Models
rpart_train | Decision trees via rpart
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
surv_reg | General Interface for Parametric Survival Models
svm_poly | General interface for polynomial support vector machines
svm_rbf | General interface for 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
update.boost_tree | General Interface for Boosted Trees
update.decision_tree | General Interface for Decision Tree Models
update.linear_reg | General Interface for Linear Regression Models
update.logistic_reg | General Interface for Logistic Regression Models
update.mars | General Interface for MARS
update.mlp | General Interface for Single Layer Neural Network
update.multinom_reg | General Interface for Multinomial Regression Models
update.rand_forest | General Interface for Random Forest Models
update.surv_reg | General Interface for Parametric Survival Models
update.svm_poly | General interface for polynomial support vector machines
update.svm_rbf | General interface for radial basis function support vector machines
varying | A placeholder function for argument values
varying_args.model_spec | Determine varying arguments
varying_args.recipe | Determine varying arguments
varying_args.step | Determine varying arguments
wa_churn | Watson Churn Data
xgb_train | Boosted trees via xgboost