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embed (version 0.1.0)

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

1,844

Version

0.1.0

License

GPL-2

Issues

22

Pull Requests

0

Stars

144

Forks

22

Maintainer

Max Kuhn

Last Published

May 25th, 2020

Functions in embed (0.1.0)

step_lencode_glm

Supervised Factor Conversions into Linear Functions using Likelihood Encodings
add_woe

Add WoE in a data frame
dictionary

Weight of evidence dictionary
step_embed

Encoding Factors into Multiple Columns
step_discretize_cart

Discretize numeric variables with CART
step_lencode_mixed

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
reexports

Objects exported from other packages
step_feature_hash

Dummy Variables Creation via Feature Hashing
step_discretize_xgb

Discretize numeric variables with XgBoost
woe_table

Crosstable with woe between a binary outcome and a predictor variable.
tunable.step_embed

tunable methods for embed
step_lencode_bayes

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
step_umap

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

Weight of evidence transformation