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parsnip (version 0.1.1)

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).

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Install

install.packages('parsnip')

Monthly Downloads

49,201

Version

0.1.1

License

GPL-2

Issues

86

Pull Requests

3

Stars

619

Forks

92

Maintainer

Max Kuhn

Last Published

May 6th, 2020

Functions in parsnip (0.1.1)

boost_tree

General Interface for Boosted Trees
mlp

General Interface for Single Layer Neural Network
mars

General Interface for MARS
linear_reg

General Interface for Linear Regression Models
keras_mlp

Simple interface to MLP models via keras
model_spec

Model Specification Information
model_fit

Model Fit Object Information
multi_predict

Model predictions across many sub-models
logistic_reg

General Interface for Logistic Regression Models
model_printer

Print helper for model objects
multinom_reg

General Interface for Multinomial Regression Models
get_model_env

Working with the parsnip model environment
nearest_neighbor

General Interface for K-Nearest Neighbor Models
fit.model_spec

Fit a Model Specification to a Dataset
surv_reg

General Interface for Parametric Survival Models
show_call

Print the model call
predict_class.model_fit

Other predict methods.
has_multi_predict

Tools for models that predict on sub-models
tidy.model_fit

Turn a parsnip model object into a tidy tibble
set_args

Change elements of a model specification
translate

Resolve a Model Specification for a Computational Engine
tidy.nullmodel

Tidy method for null models
eval_args

Evaluate parsnip model arguments
svm_poly

General interface for polynomial support vector machines
rand_forest

General Interface for Random Forest Models
null_model

General Interface for null models
rpart_train

Decision trees via rpart
predict.model_fit

Model predictions
set_engine

Declare a computational engine and specific arguments
svm_rbf

General interface for radial basis function support vector machines
make_classes

Prepend a new class
varying

A placeholder function for argument values
varying_args.model_spec

Determine varying arguments
type_sum.model_spec

Succinct summary of parsnip object
nullmodel

Fit a simple, non-informative model
set_new_model

Tools to Register Models
reexports

Objects exported from other packages
xgb_train

Boosted trees via xgboost
check_empty_ellipse

Check to ensure that ellipses are empty
add_rowindex

Add a column of row numbers to a data frame
decision_tree

General Interface for Decision Tree Models
control_parsnip

Control the fit function
descriptors

Data Set Characteristics Available when Fitting Models
convert_args

Make a table of arguments
convert_stan_interval

Convenience function for intervals
null_value

Functions required for parsnip-adjacent packages
C5.0_train

Boosted trees via C5.0