From local explanations to global understanding with explainable AI for trees

2020/01/17 by Scott M. Lundberg, Scott Lundberg, Gabriel Erion +11 · 123 citations
Computer Science · #Bayesian Modeling and Causal Inference #Explainable Artificial Intelligence (XAI)

paper · doi:10.1038/s42256-019-0138-9

crossref issued 2020/01/17 · crossref published 2020/01/17 · crossref published-online 2020/01/17 · openalex publication_date 2020/01/17 · crossref created 2020/01/17 · crossref deposited 2022/12/06 · openalex created_date 2025/10/10 · crossref indexed 2026/07/30 · openalex updated_date 2026/07/31

Cited by

• The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
• An Explainable Deep Learning Model for Focal Liver Lesion Diagnosis Using Multiparametric MRI
• Global geometry of chemical graph neural network representations in terms of chemical moieties
• Machine Learning Predicts Hepatocellular Carcinoma Risk from Routine Clinical Data: A Large Population-Based Multicentric Study
• Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies
• Modern <scp>machine‐learning</scp> for binding affinity estimation of <scp>protein–ligand</scp> complexes: Progress, opportunities, and challenges
• The timescale of nematode evolution reveals late and convergent radiations of parasitism
• Global density and biomass of arbuscular mycorrhizal fungal networks
• Predictors of loneliness at school: An explainable artificial intelligence approach on a large-scale cross-cultural assessment
• Delivering equity in low-carbon multisector infrastructure planning
• Machine learning-enhanced high-resolution exposure assessment of ultrafine particles
• Barlow Twins deep neural network for advanced 1D drug–target interaction prediction
• In situ molecular profiles of glomerular cells by integrated imaging mass spectrometry and multiplexed immunofluorescence microscopy
• Travel time prediction from sparse open data
• ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation
• Compound soil and atmospheric drought (CSAD) events and CO ₂  fluxes of a mixed deciduous forest: the occurrence, impact, and temporal contribution of main drivers
• Human-Centered Explainable AI (XAI): From Algorithms to User Experiences
• Platform-induced time-space trade-offs in ride-hailing: Multi-homing as a response to operational constraints
• OptiLIME: Optimized LIME Explanations for Diagnostic Computer Algorithms
• China’s 1 km Daily Surface Soil Moisture Fusion Dataset (2000–2025) Based on Explainable Machine Learning
• A multi‐source remote sensing approach to identify and predict delayed succession in human‐dominated tropical landscapes
• Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions
• Towards actionable hypotension prediction -- predicting catecholamine therapy initiation in the intensive care unit
• Tree Ensemble Explainability through the Hoeffding Functional Decomposition and TreeHFD Algorithm
• Moment connectedness and driving factors in the energy-food nexus: A time-frequency perspective
• Global high-resolution ultrafine particle number concentrations through data fusion with machine learning
• Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
• Global hotspots of mycorrhizal fungal richness are poorly protected
• AI-powered dissecting of carbohydrate isomers by a flexoelectric gated nanopore tweezer
• Enhanced forecasting of bird nocturnal migration intensity in relation to previous days and synoptic weather patterns
• GroupSHAP-Guided Integration of Financial News Keywords and Technical Indicators for Stock Price Prediction
• Explaining by Removing: A Unified Framework for Model Explanation
• SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism
• Competing narratives in AI ethics: a defense of sociotechnical pragmatism
• No Intelligence Without Statistics: The Invisible Backbone of Artificial Intelligence
• EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders
• Deep Learning-Based Extraction of Promising Material Groups and Common Features from High-Dimensional Data: A Case of Optical Spectra of Inorganic Crystals
• A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data
• Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging
• Enhanced lithological mapping in arid crystalline regions using explainable AI and multi-spectral remote sensing data
• Interpretable Machine Learning for Cognitive Aging: Handling Missing Data and Uncovering Social Determinant
• Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency
• SKALE: An Interpretable Multiscale Machine Learning Model for Decoding Phase‐Specific Protein Aggregation in Neurodegenerative Proteinopathies
• Faithful and Interpretable Explanations for Complex Ensemble Time Series Forecasts using Surrogate Models and Forecastability Analysis
• Increasing river sediment concentration and flux across the pan-Arctic
• Host traits and environmental factors shape infection heterogeneity in wild rat–protozoa networks
• Can Machine Learning Target Health Care Fraud? Evidence From Medicare Hospitalizations
• TreeNet: Layered Decision Ensembles
• On Predictive Explanation of Data Anomalies
• Elucidating the Grey Atmosphere: SHAP Value Analysis of a Random Forest Atmospheric Neutral Density Model
• CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from ECG
• Private and public school efficiency gaps in Latin America-A combined DEA and machine learning approach based on PISA 2022
• Determinants of Latin American students academic resilience-Insights based on PISA 2022 using an explainable machine learning approach
• A lipid atlas of the human kidney
• Fast and accurate explanations of distance-based classifiers by uncovering latent explanatory structures
• Transparency of medical artificial intelligence systems
• Model-agnostic interpretation by visualization of feature perturbations
• Data-driven composition-only machine learning for high-performance solid-state electrolytes
• A predictive model for household displacement duration after disasters
• Bridging human and machine intelligence: Reverse-engineering radiologist intentions for clinical trust and adoption
• Vocal repertoire and individuality in the plains zebra ( <i>Equus quagga</i> )
• Optimizing urban park locations with addressing environmental justice in park access and utilization by using dynamic demographic features derived from mobile phone data
• A Quantum-Chemical Bonding Database for Solid-State Materials
• The Data Gradient: Quantifying Dataset Influence in End-to-End Machine Learning Systems
• Precipitation threshold-driven shifts in dominant controls of ecosystem nitrogen retention
• Understanding the Drivers of Drought Onset and Intensification in the Canadian Prairies: Insights from Explainable Artificial Intelligence (XAI)
• Machine Learning-based beam delivery time model for Mevion 250i with Hyperscan technology
• Efficient nonparametric statistical inference on population feature importance using Shapley values
• ‘Entanglement’ – A new dynamic metric to measure team flow
• Explainable integration of process and optical parameters for plasma etch depth prediction
• Phenology-Aligned multi-task temporal fusion framework for satellite-based triple-seasonal rice yield estimation in Southeast Asia
• Photosynthetic optimum temperature plays a minor role in the increase of terrestrial carbon uptake from 2000 to 2019
• High-throughput optimized prime editing mediated endogenous protein tagging for pooled imaging of protein localization
• Noncanonical genetic markers resolve the pre-GOE emergence of aerobic bacteria in Earth’s history
• Development and validation of machine learning models to predict vancomycin- and teicoplanin-associated acute kidney injury: A retrospective, multicenter study
• Presurgical structural connectivity predicts postsurgical cognitive impairment in glioma patients
• Unifying local and global model explanations by functional decomposition of low dimensional structures
• Development of a natural language processing algorithm to extract social determinants of health from clinician notes
• BacTermFinder: a comprehensive and general bacterial terminator finder using a CNN ensemble
• Unraveling the drivers of leptospirosis risk in Thailand using machine learning
• Satellite-based evidence of recent decline in global forest recovery rate from tree mortality events
• A systematic exploration of bacterial form I rubisco maximal carboxylation rates
• Expanding the flexibility of base editing for high-throughput genetic screens in bacteria
• Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration
• Rapid changes in global river particulate organic carbon flux
• Feature selection with a lexicographic social ranking method
• Thriving Areas in Temperate Coastal Systems: Novel Insights for Marine Conservation
• Fréchet Geodesic Boosting
• Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
• Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition-Informed Approach to Quantifying Identity Fusion from Text
• Enhancing Antimicrobial Molecule Prediction via Dynamic Routing Capsule Networks and Multi-Source Molecular Embeddings
• Uncovering multi-technology convergence patterns with hypergraphs: Evolution and prediction using patent data
• Top-k Feature Importance Ranking
• Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
• Tractability Frontiers of the Shapley Value for Aggregate Conjunctive Queries
• Sampling Permutations for Shapley Value Estimation
• TwoWin-SOVA: Two Windows Discrete Cosine Transform and Synthetic Minority Oversampling Technique One-Versus-All Ensemble Classifiers for Imbalanced Hyperspectral Image Explainable Classification
• Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study
• Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection
• MetaLLMix : An XAI Aided LLM-Meta-learning Based Approach for Hyper-parameters Optimization
• Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanations
• NBDT: Neural-Backed Decision Trees
• Assessing equity in infrastructure investment distribution among U.S. cities
• Causal Beam Selection for Reliable Initial Access in AI-driven Beam Management
• Recurrent visitations reveal selectivity beyond the 15-Minute City vision
• Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery
• XAI-Driven Machine Learning System for Driving Style Recognition and Personalized Recommendations
• Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
• Delta-Audit: Explaining What Changes When Models Change
• Enhancing XAI Interpretation through a Reverse Mapping from Insights to Visualizations
• microRNA‐based predictor for diagnosis of frontotemporal dementia
• EEG-MSAF: An Interpretable Microstate Framework uncovers Default-Mode Decoherence in Early Neurodegeneration
• Shapley Values: Paired-Sampling Approximations
• UbiQTree: Uncertainty Quantification in XAI with Tree Ensembles
• Understanding Global Feature Contributions With Additive Importance Measures
• Integrating attention into explanation frameworks for language and vision transformers
• Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
• Study of Robust Features in Formulating Guidance for Heuristic Algorithms for Solving the Vehicle Routing Problem
• FIRE: Faithful Interpretable Recommendation Explanations
• Hybrid quantum tensor networks for aeroelastic applications
• Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions
• Improving Hospital Risk Prediction with Knowledge-Augmented Multimodal EHR Modeling
• Explainable Machine Learning in Credit Risk Management