Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data

• URL: http://arxiv.org/abs/2605.27618v1
• Date: Tue, 26 May 2026 19:38:10 GMT
• Title: Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
• Abstract summary: An explanation can appear plausible to humans but fail to capture the internal reasoning of a model.<n>This paper studies the trustworthiness of local explainability techniques when applied to complex tabular classification tasks.
• Score: 1.423280626666929
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the wide use of explainability techniques to attempt to understand the behavior of Artificial Intelligence (AI), the generated explanations may not always be reliable. An explanation can appear plausible to humans but fail to capture the internal reasoning of a model, particularly when dealing with complex tabular data. This paper studies the trustworthiness of local explainability techniques when applied to complex tabular classification tasks, considering evaluated metrics for three main properties: faithfulness to the model's predictions, robustness to input data variations, and complexity of the explanation itself. A benchmark was performed for Local Interpretable Model-Agnostic Explanations (LIME), Kernel SHapley Additive exPlanations (SHAP), and Feature Ablation techniques, across 32 datasets and different types of machine learning models. Model performance ranges were analyzed to identify two groups: consensus-correct, which are samples that all models predicted correctly, and consensus-wrong, samples that all models predicted incorrectly. The obtained results demonstrate that that the explanations are not always correlated with a model's predictive performance. Instead, dataset complexity and feature distributions seem to be the main factors affecting explanation quality and reliability.

Related papers

• Metamorphic Testing with the Rashomon Set: Explanation Faithfulness in Machine Learning [4.570003973862485]
  Multiple machine learning models can achieve near-equivalent predictive performance on the same task, yet provide divergent feature-based explanations.<n>This is called the Rashomon effect of (explainable) machine learning, and it raises the question of which explanations, if any, are trustworthy.<n>We propose a framework based on metamorphic testing that assesses explanation faithfulness without requiring ground-truth labels.
  arXiv  Detail & Related papers  (2026-06-04T11:57:26Z)
• Hypothesis Class Determines Explanation: Why Accurate Models Disagree on Feature Attribution [0.0]
  We find that models with identical predictive behavior can produce substantially different feature attributions.<n>We identify hypothesis class as the structural driver of this phenomenon, which we term the Explanation Lottery.<n>Our results demonstrate that model selection is not explanation-neutral: the hypothesis class chosen for deployment can determine which features are attributed responsibility for a decision.
  arXiv  Detail & Related papers  (2026-03-16T18:55:50Z)
• Does the Model Say What the Data Says? A Simple Heuristic for Model Data Alignment [0.0]
  We propose a framework to evaluate whether machine learning models align with the structure of the data they learn from.<n>Unlike existing interpretability methods that focus exclusively on explaining model behavior, our approach establishes a baseline derived directly from the data itself.
  arXiv  Detail & Related papers  (2025-11-26T21:44:55Z)
• Hard to Explain: On the Computational Hardness of In-Distribution Model Interpretation [0.9558392439655016]
  The ability to interpret Machine Learning (ML) models is becoming increasingly essential. Recent work has demonstrated that it is possible to formally assess interpretability by studying the computational complexity of explaining the decisions of various models.
  arXiv  Detail & Related papers  (2024-08-07T17:20:52Z)
• Explaining Text Similarity in Transformer Models [52.571158418102584]
  Recent advances in explainable AI have made it possible to mitigate limitations by leveraging improved explanations for Transformers. We use BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, to investigate which feature interactions drive similarity in NLP models. Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
  arXiv  Detail & Related papers  (2024-05-10T17:11:31Z)
• Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups [10.070120025381547]
  Sum-of-Parts ( SOP) is a framework that transforms any differentiable model into a group-based SANN.<n> SOP achieves state-of-the-art performance for SANNs on vision and language tasks.<n>We validate that the groups are interpretable on a range of quantitative and semantic metrics.
  arXiv  Detail & Related papers  (2023-10-25T02:50:10Z)
• 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)
• Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing [72.14557106085284]
  slice detection models (SDM) automatically identify underperforming groups of datapoints. This paper proposes a benchmark named "Discover, Explain, improve (DEIM)" for classification NLP tasks. Our evaluation shows that Edisa can accurately select error-prone datapoints with informative semantic features.
  arXiv  Detail & Related papers  (2022-11-08T19:00:00Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models [77.34726150561087]
  We define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. This paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications.
  arXiv  Detail & Related papers  (2022-03-30T05:59:50Z)
• Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set [50.67431815647126]
  Post-hoc global/local feature attribution methods are being progressively employed to understand machine learning models. We show that partial orders of local/global feature importance arise from this methodology. We show that every relation among features present in these partial orders also holds in the rankings provided by existing approaches.
  arXiv  Detail & Related papers  (2021-10-26T02:53:14Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)

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.