Learn R Programming
⚠️There's a newer version (1.1.0) of this package.Take me there.

parsnip (version 0.1.3)

A Common API to Modeling and Analysis Functions

Description

A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', etc).

Copy Link

Version

Install

install.packages('parsnip')

Monthly Downloads

49,201

Version

0.1.3

License

GPL-2

Issues

86

Pull Requests

3

Stars

618

Forks

91

Maintainer

Max Kuhn

Last Published

August 4th, 2020

Functions in parsnip (0.1.3)

add_rowindex

Add a column of row numbers to a data frame
C5.0_train

Boosted trees via C5.0
control_parsnip

Control the fit function
decision_tree

General Interface for Decision Tree Models
contr_one_hot

Contrast function for one-hot encodings
boost_tree

General Interface for Boosted Trees
descriptors

Data Set Characteristics Available when Fitting Models
check_empty_ellipse

Check to ensure that ellipses are empty
convert_stan_interval

Convenience function for intervals
null_value

Functions required for parsnip-adjacent packages
has_multi_predict

Tools for models that predict on sub-models
fit.model_spec

Fit a Model Specification to a Dataset
eval_args

Evaluate parsnip model arguments
get_model_env

Working with the parsnip model environment
glance.model_fit

Construct a single row summary "glance" of a model, fit, or other object
logistic_reg

General Interface for Logistic Regression Models
linear_reg

General Interface for Linear Regression Models
keras_mlp

Simple interface to MLP models via keras
nearest_neighbor

General Interface for K-Nearest Neighbor Models
null_model

General Interface for null models
mlp

General Interface for Single Layer Neural Network
model_fit

Model Fit Object Information
model_spec

Model Specification Information
model_printer

Print helper for model objects
predict_class.model_fit

Other predict methods.
nullmodel

Fit a simple, non-informative model
multinom_reg

General Interface for Multinomial Regression Models
mars

General Interface for MARS
multi_predict

Model predictions across many sub-models
make_classes

Prepend a new class
repair_call

Repair a model call object
reexports

Objects exported from other packages
show_call

Print the model call
set_new_model

Tools to Register Models
set_args

Change elements of a model specification
predict.model_fit

Model predictions
tidy.nullmodel

Tidy method for null models
tidy.model_fit

Turn a parsnip model object into a tidy tibble
req_pkgs

Determine required packages for a model
rpart_train

Decision trees via rpart
set_engine

Declare a computational engine and specific arguments
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._elnet

tidy methods for glmnet models
varying

A placeholder function for argument values
rand_forest

General Interface for Random Forest Models
xgb_train

Boosted trees via xgboost
translate

Resolve a Model Specification for a Computational Engine
varying_args.model_spec

Determine varying arguments
type_sum.model_spec

Succinct summary of parsnip object