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.
Some steps handle categorical predictors:
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 (
lmer). Currently implemented for numeric and two-class outcomes.
keras::layer_embeddingto 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_cart()can make binned versions of numeric predictors using supervised tree-based models.
step_pca_sparse_bayes()conduct feature extraction with sparsity of the component loadings.
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) “
data.frameProcessor 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:
Note that to use some steps, you will also have to install other
packages such as
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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