MarginalSampler: Marginal Sampler Base Class

In xplainfi: Feature Importance Methods for Global Explanations

Marginal Sampler Base Class

Description

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

xplainfi::FeatureSampler -> MarginalSampler

Methods

Public methods

• MarginalSampler$sample()

• MarginalSampler$sample_newdata()

• MarginalSampler$clone()

Inherited methods

• xplainfi::FeatureSampler$initialize()
• xplainfi::FeatureSampler$print()

---

Method 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).

---

Method 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).

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MarginalSampler$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

xplainfi documentation built on Feb. 27, 2026, 1:08 a.m.