step_lencode_glm 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.
step_lencode_glm( recipe, ..., role = NA, trained = FALSE, outcome = NULL, mapping = NULL, skip = FALSE, id = rand_id("lencode_bayes") ) # S3 method for step_lencode_glm 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 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.
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