Methods › General › Interpretability › LIME
Local Interpretable Model-Agnostic Explanations
LIME
Introduced by Marco Tulio Ribeiro et al. in "Why Should I Trust You?": Explaining the Predictions of Any Classifier
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
LIME, or Local Interpretable Model-Agnostic Explanations, is an algorithm that can explain the predictions of any classifier or regressor in a faithful way, by approximating it locally with an interpretable model. It modifies a single data sample by tweaking the feature values and observes the resulting impact on the output. It performs the role of an "explainer" to explain predictions from each data sample. The output of LIME is a set of explanations representing the contribution of each feature to a prediction for a single sample, which is a form of local interpretability.
Interpretable models in LIME can be, for instance, linear regression or decision trees, which are trained on small perturbations (e.g. adding noise, removing words, hiding parts of the image) of the original model to provide a good local approximation.
Papers archive 2025-07-28
30 shown of 378, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack 30 Jun 2025 · 1 repository · arXiv:2506.23661
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Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning 26 Jun 2025 · 0 repositories · arXiv:2506.20916
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IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems 26 Jun 2025 · 0 repositories · arXiv:2506.21310
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Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME 11 Jun 2025 · 0 repositories · arXiv:2506.10154
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Local MDI+: Local Feature Importances for Tree-Based Models 10 Jun 2025 · 0 repositories · arXiv:2506.08928
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A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI 8 Jun 2025 · 0 repositories · arXiv:2506.07234
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Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100 1 Jun 2025 · 0 repositories · arXiv:2506.06345
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DiffLIME: Enhancing Explainability with a Diffusion-Based LIME Algorithm for Fault Diagnosis 30 May 2025 · 1 repository
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Interpretable phenotyping of Heart Failure patients with Dutch discharge letters 30 May 2025 · 0 repositories · arXiv:2505.24619
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Multi-criteria Rank-based Aggregation for Explainable AI 30 May 2025 · 1 repository · arXiv:2505.24612
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MLRan: A Behavioural Dataset for Ransomware Analysis and Detection 24 May 2025 · 1 repository · arXiv:2505.18613
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Towards Trustworthy Keylogger detection: A Comprehensive Analysis of Ensemble Techniques and Feature Selections through Explainable AI 22 May 2025 · 0 repositories · arXiv:2505.16103
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Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI 21 May 2025 · 0 repositories · arXiv:2505.16028
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CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data 20 May 2025 · 0 repositories · arXiv:2505.14027
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Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks 20 May 2025 · 0 repositories · arXiv:2505.14659
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Minimizing False-Positive Attributions in Explanations of Non-Linear Models 16 May 2025 · 1 repository · arXiv:2505.11210Syntology ran 0 of 11 samples · 11 unverified
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Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques 14 May 2025 · 0 repositories · arXiv:2505.09266
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Deeply Explainable Artificial Neural Network 10 May 2025 · 0 repositories · arXiv:2505.06731
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Interactive Diabetes Risk Prediction Using Explainable Machine Learning: A Dash-Based Approach with SHAP, LIME, and Comorbidity Insights 8 May 2025 · 0 repositories · arXiv:2505.05683
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Exploring Convolutional Neural Networks for Rice Grain Classification: An Explainable AI Approach 7 May 2025 · 0 repositories · arXiv:2505.05513
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Understanding the Mechanisms Behind Structural Influences on Link Prediction: A Case Study on FB15k-237 3 May 2025 · 0 repositories · arXiv:2505.01655
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Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis 1 May 2025 · 0 repositories · arXiv:2505.00410
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Newton-Puiseux Analysis for Interpretability and Calibration of Complex-Valued Neural Networks 27 Apr 2025 · 1 repository · arXiv:2504.19176
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An Explainable Nature-Inspired Framework for Monkeypox Diagnosis: Xception Features Combined with NGBoost and African Vultures Optimization Algorithm 24 Apr 2025 · 0 repositories · arXiv:2504.17540
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What Makes for a Good Saliency Map? Comparing Strategies for Evaluating Saliency Maps in Explainable AI (XAI) 23 Apr 2025 · 0 repositories · arXiv:2504.17023
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Enhancing Multilingual Sentiment Analysis with Explainability for Sinhala, English, and Code-Mixed Content 18 Apr 2025 · 0 repositories · arXiv:2504.13545
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Long-context Non-factoid Question Answering in Indic Languages 18 Apr 2025 · 1 repository · arXiv:2504.13615
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Which LIME should I trust? Concepts, Challenges, and Solutions 31 Mar 2025 · 0 repositories · arXiv:2503.24365
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MindfulLIME: A Stable Solution for Explanations of Machine Learning Models with Enhanced Localization Precision -- A Medical Image Case Study 25 Mar 2025 · 0 repositories · arXiv:2503.20758
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EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability 22 Mar 2025 · 0 repositories · arXiv:2503.20796
Tasks archive 2025-07-28
20 shown of 228 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections