step_lencode_bayes creates a specification of a recipe step that
will convert a nominal (i.e. factor) predictor into a single set of
scores derived from a generalized linear model estimated using
step_lencode_bayes( recipe, ..., role = NA, trained = FALSE, outcome = NULL, options = list(seed = sample.int(10^5, 1)), verbose = FALSE, mapping = NULL, skip = FALSE, id = rand_id("lencode_bayes") ) # S3 method for step_lencode_bayes tidy(x, ...)
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A call to
A list of options to pass to
A logical to control the default printing by
A list of tibble results that define the
encoding. This is
A logical. Should the step be skipped when the
recipe is baked by
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added
to the sequence of existing steps (if any). For the
method, a tibble with columns
terms (the selectors or
variables for encoding),
level (the factor levels), and
value (the encodings).
For each factor predictor, a generalized linear model is fit to the outcome and the coefficients are returned as the encoding. These coefficients are on the linear predictor scale so, for factor outcomes, they are in log-odds units. The coefficients are created using a no intercept model and, when two factor outcomes are used, the log-odds reflect the event of interest being the first level of the factor.
For novel levels, a slightly timmed average of the coefficients is returned.
A hierarchical generalized linear model is fit using
rstanarm::stan_glmer() and no intercept via
stan_glmer(outcome ~ (1 | predictor), data = data, ...)
... include the
family argument (automatically
set by the step) as well as any arguments given to the
argument to the step. Relevant options include
cores, and arguments for the priors (see the links in the
prior_intercept is the argument that has the
most effect on the amount of shrinkage.
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," arXiv:1611.09477
"Hierarchical Partial Pooling for Repeated Binary Trials" https://tinyurl.com/stan-pooling
"Prior Distributions for `rstanarm`` Models" https://tinyurl.com/stan-priors
"Estimating Generalized (Non-)Linear Models with Group-Specific