embed is a package that contains extra steps for the
recipes package for embedding categorical predictors into one or more numeric columns. All of the preprocessing methods are supervised.
The steps included are:
step_embed estimates 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). Currently implemented for numeric and two-class outcomes.
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.
Some references for these methods are:
data.frameProcessor for Predictive Modeling”
To install it, use: