step_lencode_glm | R Documentation |

`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_glm") )

`recipe` |
A recipe object. The step will be added to the sequence of operations for this recipe. |

`...` |
One or more selector functions to choose variables.
For |

`role` |
Not used by this step since no new variables are created. |

`trained` |
A logical to indicate if the quantities for preprocessing have been estimated. |

`outcome` |
A call to |

`mapping` |
A list of tibble results that define the
encoding. This is |

`skip` |
A logical. Should the step be skipped when the
recipe is baked by |

`id` |
A character string that is unique to this step to identify it. |

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.

An updated version of `recipe`

with the new step added
to the sequence of existing steps (if any). For the `tidy`

method, a tibble with columns `terms`

(the selectors or
variables for encoding), `level`

(the factor levels), and
`value`

(the encodings).

When you `tidy()`

this step, a tibble with columns
`terms`

(the selectors or variables selected), `value`

and `component`

is
returned.

This step performs an supervised operation that can utilize case weights.
To use them, see the documentation in recipes::case_weights and the examples on
`tidymodels.org`

.

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

library(recipes) library(dplyr) library(modeldata) data(grants) set.seed(1) grants_other <- sample_n(grants_other, 500) reencoded <- recipe(class ~ sponsor_code, data = grants_other) %>% step_lencode_glm(sponsor_code, outcome = vars(class))

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