Research graph
References from Establishing true associations in dielectric properties: The role of mandatory consistency and dose-response relationships. Local targets link to admitted publications; unresolved targets remain external evidence.
Nonparametric feature impact and importance
2024 · External reference
Testing conditional independence in supervised learning algorithms
10.1007/s10994-021-06030-6 · 2021 · External reference
The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery
10.1145/3236386.3241340 · 2018 · External reference
All models are wrong, but many are useful: learning a variable's importance by studying an entire class of prediction models simultaneously
2019 · External reference
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
10.1093/bib/bbae379 · 2024 · External reference
A review and benchmark of feature importance methods for neural networks
10.1145/3679012 · 2024 · External reference
Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms
10.1038/s41598-024-53141-w · 2024 · External reference
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
10.1007/s10618-024-01070-7 · 2024 · External reference
General pitfalls of model-agnostic interpretation methods for machine learning models
2022 · External reference
Bias in artificial intelligence algorithms and recommendations for mitigation
10.1371/journal.pdig.0000278 · 2023 · External reference
Addressing bias in bagging and boosting regression models
10.1038/s41598-024-68907-5 · 2024 · External reference
2-step Gradient boosting approach to selectivity bias correction in tax audit: an application to the VAT gap in Italy
10.1007/s10260-022-00643-4 · 2023 · External reference
Feature importance in gradient boosting trees with cross-validation Feature selection
10.3390/e24050687 · 2022 · External reference
The mechanics of omitted variable bias: bias amplification and cancellation of offsetting biases
10.1515/jci-2016-0009 · 2016 · External reference
Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction
10.1186/s12874-023-01965-x · 2023 · External reference
A bias-variance analysis of state-of-the-art random forest text classifiers
10.1007/s11634-020-00409-4 · 2021 · External reference
Thresholding Gini variable importance with a single-trained random forest: an empirical Bayes approach
10.1016/j.csbj.2023.08.033 · 2023 · External reference
An investigation into race bias in random forest models based on breast DCE-MRI derived radiomics features
2023 · External reference
Unbiased feature selection in learning random forests for high-dimensional data
10.1155/2015/471371 · 2015 · External reference
Bias in random forest variable importance measures: illustrations, sources and a solution
10.1186/1471-2105-8-25 · 2007 · External reference
Why most discovered true associations are inflated
10.1097/ede.0b013e31818131e7 · 2008 · External reference
Prespecified falsification end points: can they validate true observational associations?
10.1001/jama.2012.96867 · 2013 · External reference
Genetic associations: false or true?
10.1016/s1471-4914(03)00030-3 · 2003 · External reference
Research techniques made simple: interpreting measures of association in clinical Research
10.1016/j.jid.2018.12.023 · 2019 · External reference
Disentangling associations between complex traits and cell types with seismic
10.1038/s41467-025-63753-z · 2025 · External reference
Evaluation of the evidence on acetaminophen use and neurodevelopmental disorders using the navigation guide methodology
10.1186/s12940-025-01208-0 · 2025 · External reference
Device-measured vigorous intermittent lifestyle physical activity (VILPA) and major adverse cardiovascular events: evidence of sex differences
10.1136/bjsports-2024-108484 · 2025 · External reference
Association between bowel movement frequency, stool consistency and MAFLD and advanced fibrosis in US adults: a cross-sectional study of NHANES 2005-2010
10.1186/s12876-024-03547-7 · 2024 · External reference
Deep Dementia Phenotyping (DEMON) Network. Data-driven discovery of associations between prescribed drugs and dementia risk: a systematic review
2025 · External reference
Model-specific feature importances: distinguishing true associations from target-feature relationships
10.1016/j.jad.2024.10.019 · 2025 · External reference
Beyond XGBoost and SHAP: unveiling true feature importance
10.1016/j.jhazmat.2025.137382 · 2025 · External reference
Stability of feature attribution: contrasting supervised and unsupervised selection for radiopathomics and TCGA outcomes
10.1016/j.canlet.2025.218133 · 2026 · External reference
The reliability gap: why high predictive accuracy doesn't guarantee stable feature importance
10.1016/j.marpolbul.2026.119398 · 2026 · External reference
Evaluating linear parametric Poisson regression vs nonparametric unsupervised learning in ulcerative colitis data
10.1053/j.gastro.2026.04.011 · 2026 · External reference
When high accuracy misleads: stability limits of supervised feature importance in QSAR biodegradation
10.1016/j.chemosphere.2026.144846 · 2026 · External reference
Beyond prediction: assessing stability in feature selection methods for materials science applications
10.1016/j.commatsci.2026.114609 · 2026 · External reference
Ensuring reliable feature importance in food chemistry AI
10.1016/j.foodchem.2026.148515 · 2026 · External reference
The absence of true association validation in supervised feature importance: evidence from battery capacity prediction for second-life applications
2026 · External reference
A leave-top-feature-out framework for validating feature importance reliability in concrete compressive strength modeling
2026 · External reference
Dataset and code for "A domain-informed automated machine learning framework for dielectric ceramic design: application to the BaTiO₃–Bi(Mg₁/₂Ti₁/₂)O₃ system
2025 · External reference
limeshap.Py
2026 · External reference
Prespecified falsification end points: can they validate true observational associations?
10.1001/jama.2012.96867 · ExternalCitation · doi-reference
2-step Gradient boosting approach to selectivity bias correction in tax audit: an application to the VAT gap in Italy
10.1007/s10260-022-00643-4 · ExternalCitation · doi-reference
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
10.1007/s10618-024-01070-7 · ExternalCitation · doi-reference
Testing conditional independence in supervised learning algorithms
10.1007/s10994-021-06030-6 · ExternalCitation · doi-reference
A bias-variance analysis of state-of-the-art random forest text classifiers
10.1007/s11634-020-00409-4 · ExternalCitation · doi-reference
Stability of feature attribution: contrasting supervised and unsupervised selection for radiopathomics and TCGA outcomes
10.1016/j.canlet.2025.218133 · ExternalCitation · doi-reference
When high accuracy misleads: stability limits of supervised feature importance in QSAR biodegradation
10.1016/j.chemosphere.2026.144846 · ExternalCitation · doi-reference
Beyond prediction: assessing stability in feature selection methods for materials science applications
10.1016/j.commatsci.2026.114609 · ExternalCitation · doi-reference
Thresholding Gini variable importance with a single-trained random forest: an empirical Bayes approach
10.1016/j.csbj.2023.08.033 · ExternalCitation · doi-reference
Ensuring reliable feature importance in food chemistry AI
10.1016/j.foodchem.2026.148515 · ExternalCitation · doi-reference
Model-specific feature importances: distinguishing true associations from target-feature relationships
10.1016/j.jad.2024.10.019 · ExternalCitation · doi-reference
Beyond XGBoost and SHAP: unveiling true feature importance
10.1016/j.jhazmat.2025.137382 · ExternalCitation · doi-reference
Research techniques made simple: interpreting measures of association in clinical Research
10.1016/j.jid.2018.12.023 · ExternalCitation · doi-reference
The reliability gap: why high predictive accuracy doesn't guarantee stable feature importance
10.1016/j.marpolbul.2026.119398 · ExternalCitation · doi-reference
Genetic associations: false or true?
10.1016/s1471-4914(03)00030-3 · ExternalCitation · doi-reference
Disentangling associations between complex traits and cell types with seismic
10.1038/s41467-025-63753-z · ExternalCitation · doi-reference
Feature importance feedback with Deep Q process in ensemble-based metaheuristic feature selection algorithms
10.1038/s41598-024-53141-w · ExternalCitation · doi-reference
Addressing bias in bagging and boosting regression models
10.1038/s41598-024-68907-5 · ExternalCitation · doi-reference
Evaluating linear parametric Poisson regression vs nonparametric unsupervised learning in ulcerative colitis data
10.1053/j.gastro.2026.04.011 · ExternalCitation · doi-reference
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
10.1093/bib/bbae379 · ExternalCitation · doi-reference
Why most discovered true associations are inflated
10.1097/ede.0b013e31818131e7 · ExternalCitation · doi-reference
Device-measured vigorous intermittent lifestyle physical activity (VILPA) and major adverse cardiovascular events: evidence of sex differences
10.1136/bjsports-2024-108484 · ExternalCitation · doi-reference
The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery
10.1145/3236386.3241340 · ExternalCitation · doi-reference
A review and benchmark of feature importance methods for neural networks
10.1145/3679012 · ExternalCitation · doi-reference
Unbiased feature selection in learning random forests for high-dimensional data
10.1155/2015/471371 · ExternalCitation · doi-reference
Bias in random forest variable importance measures: illustrations, sources and a solution
10.1186/1471-2105-8-25 · ExternalCitation · doi-reference
Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction
10.1186/s12874-023-01965-x · ExternalCitation · doi-reference
Association between bowel movement frequency, stool consistency and MAFLD and advanced fibrosis in US adults: a cross-sectional study of NHANES 2005-2010
10.1186/s12876-024-03547-7 · ExternalCitation · doi-reference
Evaluation of the evidence on acetaminophen use and neurodevelopmental disorders using the navigation guide methodology
10.1186/s12940-025-01208-0 · ExternalCitation · doi-reference
Bias in artificial intelligence algorithms and recommendations for mitigation
10.1371/journal.pdig.0000278 · ExternalCitation · doi-reference
The mechanics of omitted variable bias: bias amplification and cancellation of offsetting biases
10.1515/jci-2016-0009 · ExternalCitation · doi-reference
Feature importance in gradient boosting trees with cross-validation Feature selection
10.3390/e24050687 · ExternalCitation · doi-reference
