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embed (version 0.2.0)

Extra Recipes for Encoding Predictors

Description

Predictors can be converted to one or more numeric representations using a variety of methods. Effect encodings using simple generalized linear models or nonlinear models can be used. There are also functions for dimension reduction and other approaches.

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Install

install.packages('embed')

Monthly Downloads

1,517

Version

0.2.0

License

MIT + file LICENSE

Issues

22

Pull Requests

0

Stars

142

Forks

19

Maintainer

Emil Hvitfeldt

Last Published

April 13th, 2022

Functions in embed (0.2.0)

step_embed

Encoding Factors into Multiple Columns
step_lencode_bayes

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
step_feature_hash

Dummy Variables Creation via Feature Hashing
woe_table

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

tunable methods for embed
tidy.step_lencode_bayes

Tidy the Result of a Recipe
step_umap

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

Weight of evidence transformation
step_lencode_mixed

Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
step_lencode_glm

Supervised Factor Conversions into Linear Functions using Likelihood Encodings
step_pca_sparse

Sparse PCA Signal Extraction
step_pca_sparse_bayes

Sparse Bayesian PCA Signal Extraction
add_woe

Add WoE in a data frame
is_tf_available

Test to see if tensorflow is available
step_discretize_xgb

Discretize numeric variables with XgBoost
step_discretize_cart

Discretize numeric variables with CART
dictionary

Weight of evidence dictionary
embed-package

embed: Extra Recipes for Encoding Predictors
solubility

Compound solubility data
reexports

Objects exported from other packages
required_pkgs.step_lencode_bayes

S3 methods for tracking which additional packages are needed for steps.