Explaining Predictive Uncertainty with Information Theoretic Shapley
Values
- URL: http://arxiv.org/abs/2306.05724v2
- Date: Tue, 31 Oct 2023 17:15:40 GMT
- Title: Explaining Predictive Uncertainty with Information Theoretic Shapley
Values
- Authors: David S. Watson, Joshua O'Hara, Niek Tax, Richard Mudd, and Ido Guy
- Abstract summary: We adapt the popular Shapley value framework to explain various types of predictive uncertainty.
We implement efficient algorithms that perform well in a range of experiments on real and simulated data.
- Score: 6.49838460559032
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Researchers in explainable artificial intelligence have developed numerous
methods for helping users understand the predictions of complex supervised
learning models. By contrast, explaining the $\textit{uncertainty}$ of model
outputs has received relatively little attention. We adapt the popular Shapley
value framework to explain various types of predictive uncertainty, quantifying
each feature's contribution to the conditional entropy of individual model
outputs. We consider games with modified characteristic functions and find deep
connections between the resulting Shapley values and fundamental quantities
from information theory and conditional independence testing. We outline
inference procedures for finite sample error rate control with provable
guarantees, and implement efficient algorithms that perform well in a range of
experiments on real and simulated data. Our method has applications to
covariate shift detection, active learning, feature selection, and active
feature-value acquisition.
Related papers
- Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
This thesis is a series of independent contributions to statistics unified by a model-free perspective.
The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning.
The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
arXiv Detail & Related papers (2025-02-11T19:24:09Z) - Pattern based learning and optimisation through pricing for bin packing problem [50.83768979636913]
We argue that when problem conditions such as the distributions of random variables change, the patterns that performed well in previous circumstances may become less effective.
We propose a novel scheme to efficiently identify patterns and dynamically quantify their values for each specific condition.
Our method quantifies the value of patterns based on their ability to satisfy constraints and their effects on the objective value.
arXiv Detail & Related papers (2024-08-27T17:03:48Z) - Explainability of Machine Learning Models under Missing Data [3.0485328005356136]
Missing data is a prevalent issue that can significantly impair model performance and explainability.
This paper briefly summarizes the development of the field of missing data and investigates the effects of various imputation methods on SHAP.
arXiv Detail & Related papers (2024-06-29T11:31:09Z) - Variational Shapley Network: A Probabilistic Approach to Self-Explaining
Shapley values with Uncertainty Quantification [2.6699011287124366]
Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes.
We introduce a novel, self-explaining method that simplifies the computation of Shapley values significantly, requiring only a single forward pass.
arXiv Detail & Related papers (2024-02-06T18:09:05Z) - LLpowershap: Logistic Loss-based Automated Shapley Values Feature
Selection Method [0.0]
We present a novel feature selection method, LLpowershap, which makes use of loss-based Shapley values to identify informative features with minimal noise.
Our simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods.
arXiv Detail & Related papers (2024-01-23T11:46:52Z) - Fast Shapley Value Estimation: A Unified Approach [71.92014859992263]
We propose a straightforward and efficient Shapley estimator, SimSHAP, by eliminating redundant techniques.
In our analysis of existing approaches, we observe that estimators can be unified as a linear transformation of randomly summed values from feature subsets.
Our experiments validate the effectiveness of our SimSHAP, which significantly accelerates the computation of accurate Shapley values.
arXiv Detail & Related papers (2023-11-02T06:09:24Z) - LaPLACE: Probabilistic Local Model-Agnostic Causal Explanations [1.0370398945228227]
We introduce LaPLACE-explainer, designed to provide probabilistic cause-and-effect explanations for machine learning models.
The LaPLACE-Explainer component leverages the concept of a Markov blanket to establish statistical boundaries between relevant and non-relevant features.
Our approach offers causal explanations and outperforms LIME and SHAP in terms of local accuracy and consistency of explained features.
arXiv Detail & Related papers (2023-10-01T04:09:59Z) - VCNet: A self-explaining model for realistic counterfactual generation [52.77024349608834]
Counterfactual explanation is a class of methods to make local explanations of machine learning decisions.
We present VCNet-Variational Counter Net, a model architecture that combines a predictor and a counterfactual generator.
We show that VCNet is able to both generate predictions, and to generate counterfactual explanations without having to solve another minimisation problem.
arXiv Detail & Related papers (2022-12-21T08:45:32Z) - MACE: An Efficient Model-Agnostic Framework for Counterfactual
Explanation [132.77005365032468]
We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE)
In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity.
Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
arXiv Detail & Related papers (2022-05-31T04:57:06Z) - Beyond Trivial Counterfactual Explanations with Diverse Valuable
Explanations [64.85696493596821]
In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction.
We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss.
Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
arXiv Detail & Related papers (2021-03-18T12:57:34Z) - Explaining predictive models using Shapley values and non-parametric
vine copulas [2.6774008509840996]
We propose two new approaches for modelling the dependence between the features.
The performance of the proposed methods is evaluated on simulated data sets and a real data set.
Experiments demonstrate that the vine copula approaches give more accurate approximations to the true Shapley values than its competitors.
arXiv Detail & Related papers (2021-02-12T09:43:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.