embed
has extra steps for the
recipes
package for embedding
predictors into one or more numeric columns. Almost all of the
preprocessing methods are supervised.
These steps are available here in a separate package because the step
dependencies, rstanarm
,
lme4
, and
keras
, are fairly heavy.
Some steps handle categorical predictors:
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.
step_feature_hash()
can create indicator variables using feature
hashing.
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.
step_discretize_xgb()
and step_discretize_cart()
can make binned
versions of numeric predictors using supervised tree-based models.
step_pca_sparse()
and step_pca_sparse_bayes()
conduct feature
extraction with sparsity of the component loadings.
Some references for these methods are:
vtreat
: a data.frame
Processor for
Predictive Modeling”There are two articles that walk through how to use these embedding steps, using generalized linear models and neural networks built via TensorFlow.
To install the package:
install.packages("embed")
Note that to use some steps, you will also have to install other
packages such as rstanarm
and lme4
. For all of the steps to work,
you may want to use:
install.packages(c("rpart", "xgboost", "rstanarm", "lme4"))
To get a bug fix or to use a feature from the development version, you can install the development version of this package from GitHub.
# install.packages("remotes")
remotes::install_github("tidymodels/embed")
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