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embed (version 0.0.4)

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 . All encoding methods are supervised.

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Version

Install

install.packages('embed')

Monthly Downloads

1,844

Version

0.0.4

License

GPL-2

Maintainer

Max Kuhn

Last Published

September 15th, 2019

Functions in embed (0.0.4)

step_umap

Supervised and unsupervised uniform manifold approximation and projection (UMAP)
reexports

Objects exported from other packages
woe_table

Crosstable with woe between a binary outcome and a predictor variable.
add_woe

Add WoE in a data frame
dictionary

Weight of evidence dictionary
step_lencode_glm

Supervised Factor Conversions into Linear Functions using Likelihood Encodings
step_lencode_mixed

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
step_woe

Weight of evidence transformation
step_lencode_bayes

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
step_embed

Encoding Factors into Multiple Columns