Encodes columns of type
Impact coding for classification Tasks converts factor levels of each (factorial) column to the difference between each target level's conditional log-likelihood given this level, and the target level's global log-likelihood.
Impact coding for regression Tasks converts factor levels of each (factorial) column to the difference between the target's conditional mean given this level, and the target's global mean.
Treats new levels during prediction like missing values.
R6Class object inheriting from
PipeOpEncodeImpact$new(id = "encodeimpact", param_vals = list())
Identifier of resulting object, default
param_vals :: named
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default
Input and output channels are inherited from
The output is the input
Task with all affected
ordered parameters encoded.
$state is a named
list with the
$state elements inherited from
PipeOpTaskPreproc, as well as:
impact :: a named
A list with an element for each affected feature:
For regression each element is a single column matrix of impact values for each level of that feature.
For classification, it is a list with an element for each feature level, which is a vector giving the impact of this feature level on each outcome level.
A finite positive value used for smoothing. Mostly relevant for classification Tasks if a factor does not coincide with a target factor level (and would otherwise give an infinite logit value). Initialized to
TRUE, impute missing values as impact 0; otherwise the respective impact is coded as
Uses Laplace smoothing, mostly to avoid infinite values for classification Task.
Only methods inherited
library("mlr3") poe = po("encodeimpact") task = TaskClassif$new("task", data.table::data.table( x = factor(c("a", "a", "a", "b", "b")), y = factor(c("a", "a", "b", "b", "b"))), "x") poe$train(list(task))[]$data() poe$state
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