Marginal Sampler Base Class — MarginalSampler • xplainfi

Abstract base class for marginal sampling strategies that do not condition on other features. Marginal samplers sample from P(X_S), the marginal distribution of features S, ignoring any dependencies with other features.

Details

This class provides a common interface for different marginal sampling approaches:

MarginalPermutationSampler: Shuffles features independently within the dataset
MarginalReferenceSampler: Samples complete rows from reference data

Both approaches sample from the marginal distribution P(X_S), but differ in how they preserve or break within-row dependencies:

Permutation breaks ALL dependencies (both with target and between features)
Reference sampling preserves WITHIN-row dependencies but breaks dependencies with test data

Comparison with ConditionalSampler:

MarginalSampler: Samples from $P(X_S)$ - no conditioning
ConditionalSampler: Samples from $P(X_S | X_{-S})$- conditions on other features

This base class implements the public $sample() and $sample_newdata() methods, delegating to private .sample_marginal() which subclasses must implement.

Super class

FeatureSampler -> MarginalSampler

Methods

Inherited methods

`MarginalSampler$sample()`

Sample features from their marginal distribution.

Usage

MarginalSampler$sample(feature, row_ids = NULL)

Arguments

feature: (character()) Feature name(s) to sample.
row_ids: (integer() | NULL) Row IDs from task to use.

Returns

Modified copy with sampled feature(s).

`MarginalSampler$sample_newdata()`

Sample from external data.

Usage

MarginalSampler$sample_newdata(feature, newdata)

Arguments

feature: (character()) Feature(s) to sample.
newdata: (data.table) External data to use.

Returns

Modified copy with sampled feature(s).

`MarginalSampler$clone()`

The objects of this class are cloneable with this method.

Usage

MarginalSampler$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.