embed v0.1.2

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by Max Kuhn View Source

Extra Recipes for Encoding Categorical Predictors

Predictors can be converted to one or more numeric representations using simple generalized linear models <arXiv:1611.09477> or nonlinear models <arXiv:1604.06737>. Most encoding methods are supervised.

Functions in embed

Name	Description
required_pkgs.step_lencode_bayes	S3 methods for tracking which additional packages are needed for steps.
dictionary	Weight of evidence dictionary
add_woe	Add WoE in a data frame
is_tf_available	Test to see if tensorflow is available
reexports	Objects exported from other packages
step_embed	Encoding Factors into Multiple Columns
step_discretize_cart	Discretize numeric variables with CART
step_discretize_xgb	Discretize numeric variables with XgBoost
step_feature_hash	Dummy Variables Creation via Feature Hashing
step_lencode_bayes	Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
step_lencode_glm	Supervised Factor Conversions into Linear Functions using Likelihood Encodings
step_umap	Supervised and unsupervised uniform manifold approximation and projection (UMAP)
step_woe	Weight of evidence transformation
woe_table	Crosstable with woe between a binary outcome and a predictor variable.
tunable.step_embed	tunable methods for embed
step_lencode_mixed	Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
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Details

License	GPL-2
Encoding	UTF-8
LazyData	true
RoxygenNote	7.1.1
ByteCompile	true
URL	https://embed.tidymodels.org, https://github.com/tidymodels/embed
BugReports	https://github.com/tidymodels/embed/issues
NeedsCompilation	no
Packaged	2020-10-17 16:50:00 UTC; max
Repository	CRAN
Date/Publication	2020-10-17 17:20:02 UTC

suggests	covr , ggplot2 , knitr , modeldata , rmarkdown , rpart , testthat , xgboost
imports	dplyr , generics , keras , lme4 , purrr , rlang , rsample , rstanarm , stats , tensorflow , tibble , tidyr , utils , uwot , withr
depends	R (>= 3.1) , recipes (>= 0.1.8)
Contributors	RStudio

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