Base class for implementing different sampling strategies for feature importance methods like PFI and CFI
Public fields
task(mlr3::Task) Original task.
label(
character(1)) Name of the sampler.feature_types(
character()) Feature types supported by the sampler. Will be checked against the provied mlr3::Task to ensure compatibility.param_set(paradox::ParamSet) Parameter set for the sampler.
Methods
Method new()
Creates a new instance of the FeatureSampler class
Usage
FeatureSampler$new(task)Arguments
task(mlr3::Task) Task to sample from
Method sample()
Sample values for feature(s) from stored task
Returns
Modified copy of the input features with the feature(s) sampled:
A data.table with same number of columns and one row matching the supplied row_ids
Method sample_newdata()
Sample values for feature(s) using external data
Arguments
feature(
character) Feature name(s) to sample (can be single or multiple)newdata(
data.table) External data to use for sampling.