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 | |
| No Results! | ||
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Details
| 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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