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recipes

Preprocessing and Feature Engineering Steps for Modeling

Installation

About

A recipe prepares your data for modeling. We provide an extensible framework for pipeable sequences of feature engineering steps provides preprocessing tools to be applied to data. Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting processed output can then be used as inputs for statistical or machine learning models.

github.com/tidymodels/recipes
recipes.tidymodels.org/
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Key Metrics

Version 1.0.10
R ≥ 3.6
Published 2024-02-18 61 days ago
Needs compilation? no
License MIT
License File
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Maintainer

Maintainer

Max Kuhn

max@posit.co

Authors

Max Kuhn

aut / cre

Hadley Wickham

aut

Emil Hvitfeldt

aut

Posit Software
PBC

cph / fnd

Material

NEWS
Reference manual
Package source

Vignettes

Handling categorical predictors
Ordering of steps
Roles in recipes
Selecting variables
On skipping steps
Introduction to recipes

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

recipes archive

Depends

dplyr ≥ 1.1.0
R ≥ 3.6

Imports

cli
clock ≥ 0.6.1
ellipsis
generics ≥ 0.1.2
glue
gower
hardhat ≥ 1.3.0
ipred ≥ 0.9-12
lifecycle ≥1.0.3
lubridate ≥ 1.8.0
magrittr
Matrix
purrr ≥1.0.0
rlang ≥ 1.1.0
stats
tibble
tidyr ≥ 1.0.0
tidyselect ≥ 1.2.0
timeDate
utils
vctrs ≥ 0.5.0
withr

Suggests

covr
ddalpha
dials ≥ 1.2.0
ggplot2
igraph
kernlab
knitr
modeldata ≥ 0.1.1
parsnip ≥ 1.2.0
RANN
RcppRoll
rmarkdown
rpart
rsample
RSpectra
splines2
testthat ≥ 3.0.0
workflows
xml2

Reverse Depends

embed
shinyrecipes
textrecipes
themis

Reverse Imports

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bestNormalize
card
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MLDataR
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modeltime.resample
nestedmodels
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stacks
text
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usemodels
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viralmodels
viralx
viruslearner

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