konradsemsch / embed-1

Extra recipes for categorical predictor embeddings

embed is a package that contains extra steps for the recipes package for embedding predictors into one or more numeric columns. All of the preprocessing methods are supervised.

These steps are contained in a separate package because the package dependencies, rstanarm, lme4, and keras, are fairly heavy.

The steps for categorical predictors are:

• step_lencode_glm, step_lencode_bayes, and step_lencode_mixed estimate the effect of each of the factor levels on the outcome and these estimates are used as the new encoding. The estimates are estimated by a generalized linear model. This step can be executed without pooling (via glm) or with partial pooling (stan_glm or lmer). Currently implemented for numeric and two-class outcomes.

• step_embed uses keras::layer_embedding to translate the original C factor levels into a set of D new variables (< C). The model fitting routine optimizes which factor levels are mapped to each of the new variables as well as the corresponding regression coefficients (i.e., neural network weights) that will be used as the new encodings.

• step_woe creates new variables based on weight of evidence encodings.

For numeric predictors:

• step_umap uses a nonlinear transformation similar to t-SNE but can be used to project the transformation on new data. Both supervised and unsupervised methods can be used.

Some references for these methods are:

• Francois C and Allaire JJ (2018) Deep Learning with R, Manning
• Guo, C and Berkhahn F (2016) "Entity Embeddings of Categorical Variables"
• Micci-Barreca D (2001) "A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems," ACM SIGKDD Explorations Newsletter, 3(1), 27-32.
• Zumel N and Mount J (2017) "vtreat: a data.frame Processor for Predictive Modeling"
• McInnes L and Healy J (2018) UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Good, I. J. (1985), "Weight of evidence: A brief survey", Bayesian Statistics, 2, pp.249-270.

To install the package:

install.packages("embed")

## for development version:
require("devtools")
install_github("tidymodels/embed")