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embed (version 0.1.2)

Extra Recipes for Encoding Categorical Predictors

Description

Predictors can be converted to one or more numeric representations using simple generalized linear models or nonlinear models . Most encoding methods are supervised.

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Install

install.packages('embed')

Monthly Downloads

1,183

Version

0.1.2

License

GPL-2

Issues

23

Pull Requests

0

Stars

143

Forks

19

Maintainer

Max Kuhn

Last Published

October 17th, 2020

Functions in embed (0.1.2)

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