embed v0.0.3

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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>. All encoding methods are supervised.

Functions in embed

Name Description
woe_table Crosstable with woe between a binary outcome and a predictor variable.
reexports Objects exported from other packages
step_lencode_mixed Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
step_woe Weight of evidence transformation
step_embed Encoding Factors into Multiple Columns
step_lencode_bayes Supervised Factor Conversions into Linear Functions using Bayesian Likelihood Encodings
dictionary Weight of evidence dictionary
step_umap Supervised and unsupervised uniform manifold approximation and projection (UMAP)
add_woe Add WoE in a data frame
step_lencode_glm Supervised Factor Conversions into Linear Functions using Likelihood Encodings
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License GPL-2
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
ByteCompile true
URL https://tidymodels.github.io/embed
BugReports https://github.com/tidymodels/embed/issues
NeedsCompilation no
Packaged 2019-07-12 16:31:55 UTC; max
Repository CRAN
Date/Publication 2019-07-12 17:00:03 UTC

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