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Title: GitHub - interpretml/interpret: Fit interpretable models. Explain blackbox machine learning.

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Description: Fit interpretable models. Explain blackbox machine learning. - GitHub - interpretml/interpret: Fit interpretable models. Explain blackbox machine learning.

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*https://patch-diff.githubusercontent.com/interpretml/interpret#citations
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https://patch-diff.githubusercontent.com/interpretml/interpret#supported-techniques
Explainable Boostinghttps://interpret.ml/docs/ebm.html
APLRhttps://interpret.ml/docs/aplr.html
Decision Treehttps://interpret.ml/docs/dt.html
Decision Rule Listhttps://interpret.ml/docs/dr.html
Linear/Logistic Regressionhttps://interpret.ml/docs/lr.html
SHAP Kernel Explainerhttps://interpret.ml/docs/shap.html
LIMEhttps://interpret.ml/docs/lime.html
Morris Sensitivity Analysishttps://interpret.ml/docs/msa.html
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https://patch-diff.githubusercontent.com/interpretml/interpret#train-a-glassbox-model
https://patch-diff.githubusercontent.com/interpretml/interpret/blob/main/docs/readme/ebm-global.png?raw=true
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DP-EBMshttps://proceedings.mlr.press/v139/nori21a/nori21a.pdf
documentationhttps://interpret.ml/docs
https://interpret.ml/docs/python/examples/custom-interactions.htmlhttps://interpret.ml/docs/python/examples/custom-interactions.html
classification EBMshttps://learn.microsoft.com/en-us/fabric/data-science/explainable-boosting-machines-classification
regression EBMshttps://learn.microsoft.com/en-us/fabric/data-science/explainable-boosting-machines-regression
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https://patch-diff.githubusercontent.com/interpretml/interpret#citations
Paper linkhttps://arxiv.org/pdf/1909.09223.pdf
Paper linkhttps://www.microsoft.com/en-us/research/wp-content/uploads/2017/06/KDD2015FinalDraftIntelligibleModels4HealthCare_igt143e-caruanaA.pdf
Paper linkhttps://www.cs.cornell.edu/~yinlou/papers/lou-kdd13.pdf
Paper linkhttps://www.cs.cornell.edu/~yinlou/papers/lou-kdd12.pdf
Paper linkhttps://arxiv.org/pdf/2206.15465.pdf
Paper linkhttps://arxiv.org/pdf/1810.09092.pdf
Paper linkhttps://arxiv.org/pdf/1710.06169
Paper linkhttps://arxiv.org/pdf/1911.04974.pdf
Paper linkhttps://www.microsoft.com/en-us/research/publication/interpreting-interpretability-understanding-data-scientists-use-of-interpretability-tools-for-machine-learning/
Paper linkhttps://arxiv.org/pdf/2006.06466.pdf
Paper linkhttps://proceedings.mlr.press/v139/nori21a/nori21a.pdf
Paper linkhttps://arxiv.org/pdf/1602.04938.pdf
Paper linkhttps://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf
Paper linkhttps://arxiv.org/pdf/1802.03888
Paper linkhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6467492/pdf/nihms-1505578.pdf
Paper linkhttps://www.researchgate.net/profile/Will_Usher/publication/312204236_SALib_An_open-source_Python_library_for_Sensitivity_Analysis/links/5ac732d64585151e80a39547/SALib-An-open-source-Python-library-for-Sensitivity-Analysis.pdf?origin=publication_detail
Paper linkhttps://abe.ufl.edu/Faculty/jjones/ABE_5646/2010/Morris.1991%20SA%20paper.pdf
Paper linkhttps://projecteuclid.org/download/pdf_1/euclid.aos/1013203451
Paper linkhttps://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf
Linkhttps://plot.ly
Linkhttps://joblib.readthedocs.io/en/latest/
https://patch-diff.githubusercontent.com/interpretml/interpret#videos
The Science Behind InterpretML: Explainable Boosting Machinehttps://www.youtube.com/watch?v=MREiHgHgl0k
How to Explain Models with InterpretML Deep Divehttps://www.youtube.com/watch?v=WwBeKMQ0-I8
Black-Box and Glass-Box Explanation in Machine Learninghttps://youtu.be/7uzNKY8pEhQ
Explainable AI explained! By-design interpretable models with Microsofts InterpretMLhttps://www.youtube.com/watch?v=qPn9m30ojfc
Interpreting Machine Learning Models with InterpretMLhttps://www.youtube.com/watch?v=ERNuFfsknhk
Machine Learning Model Interpretability using AzureML & InterpretML (Explainable Boosting Machine)https://www.youtube.com/watch?v=0ocVtXU8o1I
A Case Study of Using Explainable Boosting Machineshttps://uncch.hosted.panopto.com/Panopto/Pages/Embed.aspx?id=063d6839-e8db-40e0-8df4-b0fc012e709b&start=0
From SHAP to EBM: Explain your Gradient Boosting Models in Pythonhttps://www.youtube.com/watch?v=hnZjw77-1rE
Rich Caruana – Friends Don’t Let Friends Deploy Black-Box Modelshttps://www.youtube.com/watch?v=2YKtNYBuojE
https://patch-diff.githubusercontent.com/interpretml/interpret#external-links
Machine Learning Interpretability in Banking: Why It Matters and How Explainable Boosting Machines Can Helphttps://www.prometeia.com/en/trending-topics-article/machine-learning-interpretability-in-banking-why-it-matters-and-how-explainable-boosting-machines-can-help
Interpretable Machine Learning – Increase Trust and Eliminate Biashttps://ficonsulting.com/insight-post/interpretable-machine-learning-increase-trust-and-eliminate-bias/
Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurancehttps://arxiv.org/pdf/2503.21321
Enhancing Trust in Credit Risk Models: A Comparative Analysis of EBMs and GBMshttps://2os.medium.com/enhancing-trust-in-credit-risk-models-a-comparative-analysis-of-ebms-and-gbms-25e02810300f
Explainable AI: unlocking value in FEC operationshttps://analytiqal.nl/2024/01/22/fec-value-from-explainable-ai/
Interpretable or Accurate? Why Not Both?https://towardsdatascience.com/interpretable-or-accurate-why-not-both-4d9c73512192
The Explainable Boosting Machine. As accurate as gradient boosting, as interpretable as linear regression.https://towardsdatascience.com/the-explainable-boosting-machine-f24152509ebb
Exploring explainable boosting machineshttps://leinadj.github.io/2023/04/09/Exploring-Explainable-Boosting-Machines.html
Performance And Explainability With EBMhttps://blog.oakbits.com/ebm-algorithm.html
InterpretML: Another Way to Explain Your Modelhttps://towardsdatascience.com/interpretml-another-way-to-explain-your-model-b7faf0a384f8
A gentle introduction to GA2Ms, a white box modelhttps://www.fiddler.ai/blog/a-gentle-introduction-to-ga2ms-a-white-box-model
Explaining Non-Parametric Additive Modelshttps://gablabc.github.io/posts/2025/01/NonParametricAdditive/
Model Interpretation with Microsoft’s Interpret MLhttps://medium.com/@sand.mayur/model-interpretation-with-microsofts-interpret-ml-85aa0ad697ae
Explaining Model Pipelines With InterpretMLhttps://medium.com/@mariusvadeika/explaining-model-pipelines-with-interpretml-a9214f75400b
Explain Your Model with Microsoft’s InterpretMLhttps://medium.com/@Dataman.ai/explain-your-model-with-microsofts-interpretml-5daab1d693b4
On Model Explainability: From LIME, SHAP, to Explainable Boostinghttps://everdark.github.io/k9/notebooks/ml/model_explain/model_explain.nb.html
Dealing with Imbalanced Data (Mortgage loans defaults)https://mikewlange.github.io/ImbalancedData-/index.html
The right way to compute your Shapley Valueshttps://towardsdatascience.com/the-right-way-to-compute-your-shapley-values-cfea30509254
The Art of Sprezzatura for Machine Learninghttps://towardsdatascience.com/the-art-of-sprezzatura-for-machine-learning-e2494c0db727
Mixing Art into the Science of Model Explainabilityhttps://towardsdatascience.com/mixing-art-into-the-science-of-model-explainability-312b8216fa95
Automatic Piecewise Linear Regressionhttps://link.springer.com/article/10.1007/s00180-024-01475-4
MCTS EDA which makes sensehttps://www.kaggle.com/code/ambrosm/mcts-eda-which-makes-sense/notebook
Explainable Boosting machines for Tabular datahttps://www.kaggle.com/code/parulpandey/explainable-boosting-machines-for-tabular-data
https://patch-diff.githubusercontent.com/interpretml/interpret#papers-that-use-or-compare-ebms
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Modelshttps://link.springer.com/article/10.1007/s12599-024-00922-2
The hidden risk of round numbers and sharp thresholds in clinical practicehttps://www.nature.com/articles/s41746-025-02079-y
TabArena: A Living Benchmark for Machine Learning on Tabular Datahttps://arxiv.org/pdf/2506.16791
Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurancehttps://arxiv.org/pdf/2503.21321
Toward Faithful Retrieval-Augmented Generation with Sparse Autoencodershttps://arxiv.org/html/2512.08892v1
Modelling Container Transhipment Throughput and Analysing Dynamics During Significant External Events: A Case Study of the Port of Busan and the China-to-U.S. Trade Routehttps://eprints.soton.ac.uk/505005/1/_PDF_A_Modelling_Container_Transhipment_Throughput_and_Analysing_Dynamics_During_Significant_External_Events_-_A_Case_Study_of_the_Port_of_Busan_and_the_China-to-U.S._Trade_Route.pdf
The Most Important Features in Generalized Additive Models Might Be Groups of Featureshttps://arxiv.org/pdf/2506.19937
GAMFORMER: In-context Learning for Generalized Additive Modelshttps://arxiv.org/pdf/2410.04560v1
Beyond black-box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networkshttps://arxiv.org/pdf/2504.08712
Glass Box Machine Learning and Corporate Bond Returnshttps://download.ssrn.com/2024/12/7/5047456.pdf?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEJn%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIQC4Tahz9gLK1PnaT2OcGVvU95LIXVjM6MGGLiR8muLBHwIgDhBNcW1HFouaDPID%2FgMGtfAOIqCtr0JTXSsBYYGjXZEqvQUIYhAEGgwzMDg0NzUzMDEyNTciDBSJqXVnefWlpTvEvSqaBfTjYUxrqKGMDY1QImthn4LR848sVKt0vRNJSuhmBUct5KZ%2FYHehm4HVsRgxd%2FYezoCGxoo%2Bee1rhSCW7WVwdPrNzvAb34a410A6DkywgnsGsKhvMltoeYudsXrL2SlqY6fP5z8mmzELDBjHhNRbpjaPx%2BmHQiv8PrE6bqrQz%2Fe18Aj9JVsAlUAiJ0s9AiK5kaqJU6yZJJvaFh4AWHd8IsOj0QBU%2BHoTYj5ff2XzM9PtWUQbZccke066NcJEuUSE7fj5OREKYi%2BCXG6zNWu8y4BubfSzjL1pLRDuBB%2Fc6zQNWu%2Bz9sibZRzBvx1mmhTrL2ffhWCeTlCaSj1EsM3VuyNz3d8z2MFTpxn4hBqcR3lk0daL4qllASz3UMp%2FPwteZszzpE9moZwEwJWmR4TrH3KFF7x2bZaQPLvgt%2BqnstHyJXOgaEmXDX0yEyGQyDQh9RpD4n%2FlBTSHsXme0OfjEt5AscRnBqRpsX3ZG9Bx0cK5ibthJ5M%2FwsExm76cF8tPdyOCqozaF%2B9l1sJsWK1h4jHYrbdyZZZtKj786Ed2CAOZ0M%2FzY1hyHHAO47jOlNs9Ju2qLhsdXmcbFRiBfA2IN4UxClTyrAaYA2fQhpFHpRdM5CMk8%2Fe4h8Mt1PVO7K95BihB2P5O%2BCiWWzrlwAVKa5KKipxmRTDTjl2VIKFkwLeCwPHJFpQCL1ZknhGyIiD9hLpS%2BykAXnHyN4U7dN6rtOf8FGqDO0QUA2ZvTaT6DXrUps1Wf2iNqk%2Facl3RyB3nNwRw4igeUx9UysRRFsz2Rs93kZJq17yGiCLP38xF%2FniKj8doporrNbBjkMhBpAXKyqDpQ7JQ%2Bwu8GPAWdPiD%2FumnjvuuoG0UkmGX81izy5zCREKOraIuDzqOMjD6%2BIu7BjqxAYNSOCdUDYw30aHTCSDBoj%2B4LzQk2ZMdumvEyvNKv%2ButPmI1J6LsIwV8s1q7dgqpe2%2FA%2BLX4ZbWaT0OPR3idfz7ADSNZp6Ykfinfj6FSf673Kj%2FOm4etpiZxcMy8MDD65CHXCDy0%2FhMz9JAwmDyD2Dmb%2Fhvta226gohBnpuBA8KCoP946GdYEzyaXGvByrO4BOmvB%2BlRifauewFHndym7seWzQtJ%2B514aguu1JT%2F7XJVUw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241218T174139Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAUPUUPRWE45GYNSPF%2F20241218%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=2c2b0fd799efafee1598fbc8ba7dd050451f67845f5cfa3b01a5b3ed0db7db1c&abstractId=5047456
Data Science with LLMs and Interpretable Modelshttps://arxiv.org/pdf/2402.14474v1.pdf
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machinehttps://arxiv.org/pdf/2402.06885.pdf
Distill knowledge of additive tree models into generalized linear modelshttps://detralytics.com/wp-content/uploads/2023/10/Detra-Note_Additive-tree-ensembles.pdf
Explainable Boosting Machines with Sparsity - Maintaining Explainability in High-Dimensional Settingshttps://arxiv.org/abs/2311.07452
Cost of Explainability in AI: An Example with Credit Scoring Modelshttps://link.springer.com/chapter/10.1007/978-3-031-44064-9_26
Explainable Boosting Machine for Structural Health Assessment: An Interpretable Approach to Data-Driven Structural Assessmenthttps://dpi-proceedings.com.destechpub.a2hosted.com/index.php/shm2025/article/view/37379/35953
Interpretable Machine Learning Leverages Proteomics to Improve Cardiovascular Disease Risk Prediction and Biomarker Identificationhttps://www.medrxiv.org/content/10.1101/2024.01.12.24301213v1.full.pdf
Interpretable Additive Tabular Transformer Networkshttps://openreview.net/pdf/d2f0db2646418b24bb322fc1f4082fd9e65409c2.pdf
Signature Informed Sampling for Transcriptomic Datahttps://www.biorxiv.org/content/biorxiv/early/2023/10/31/2023.10.26.564263.full.pdf
Interpretable Survival Analysis for Heart Failure Risk Predictionhttps://arxiv.org/pdf/2310.15472.pdf
Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approachhttps://www.jmir.org/2025/1/e68030
Analyzing User Characteristics of Hate Speech Spreaders on Social Mediahttps://arxiv.org/pdf/2310.15772
Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritishttps://www.mdpi.com/1648-9144/61/5/833
AI-Based Estimation and Segmentation of Biological Age Using Clinical Datahttps://doi.org/10.21203/rs.3.rs-6638646/v1
Actionable and diverse counterfactual explanations incorporating domain knowledge and causal constraintshttps://arxiv.org/html/2511.20236v1
Explainable Learning Framework for the Assessment and Prediction of Wind Shear-Induced Aviation Turbulencehttps://www.mdpi.com/2073-4433/16/12/1318
HearteXplain: explainable prediction of acute heart failure and identification of hematologic biomarkers using EBMs and Morris sensitivity analysishttps://www.nature.com/articles/s41598-025-23668-7
Identification of a Novel Lipidomic Biomarker for Hepatocyte Carcinoma Diagnosis: Advanced Boosting Machine Learning Techniques Integrated with Explainable Artificial Intelligencehttps://www.mdpi.com/2218-1989/15/11/716
Interpretable machine learning for precision cognitive aginghttps://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2025.1560064/full
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairshttps://arxiv.org/pdf/2308.01157.pdf
Model Interpretability in Credit Insurancehttp://hdl.handle.net/10400.5/27507
Enhancing ML Interpretability for Credit Scoringhttps://arxiv.org/html/2509.11389v1
Transparent and Fair Profiling in Employment Services: Evidence from Switzerlandhttps://www.arxiv.org/pdf/2509.11847
Federated Boosted Decision Trees with Differential Privacyhttps://arxiv.org/pdf/2210.02910.pdf
Differentially private and explainable boosting machine with enhanced utilityhttps://www.sciencedirect.com/science/article/abs/pii/S0925231224011950?via%3Dihub#preview-section-abstract
Balancing Explainability and Privacy in Bank Failure Prediction: A Differentially Private Glass-Box Approachhttps://ieeexplore.ieee.org/abstract/document/10818483
GAM(E) CHANGER OR NOT? AN EVALUATION OF INTERPRETABLE MACHINE LEARNING MODELShttps://arxiv.org/pdf/2204.09123.pdf
GAM Coach: Towards Interactive and User-centered Algorithmic Recoursehttps://arxiv.org/pdf/2302.14165.pdf
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?https://arxiv.org/pdf/2304.11749v1.pdf
Practice and Challenges in Building a Universal Search Quality Metrichttps://www.researchgate.net/profile/Nuo-Chen-38/publication/370126720_Practice_and_Challenges_in_Building_a_Universal_Search_Quality_Metric/links/6440a0f239aa471a524cb77d/Practice-and-Challenges-in-Building-a-Universal-Search-Quality-Metric.pdf?origin=publication_detail
Explaining Phishing Attacks: An XAI Approach to Enhance User Awareness and Trusthttps://www.researchgate.net/profile/Giuseppe-Desolda/publication/370003878_Explaining_Phishing_Attacks_An_XAI_Approach_to_Enhance_User_Awareness_and_Trust/links/643922a8e881690c4bd50ced/Explaining-Phishing-Attacks-An-XAI-Approach-to-Enhance-User-Awareness-and-Trust.pdf
Revealing the Galaxy-Halo Connection Through Machine Learninghttps://arxiv.org/pdf/2204.10332.pdf
How the Galaxy–Halo Connection Depends on Large-Scale Environmenthttps://arxiv.org/pdf/2402.07995.pdf
Unveiling the drivers of the Baryon Cycles with Interpretable Multi-step Machine Learning and Simulationshttps://arxiv.org/pdf/2504.09744v1
Explainable Artificial Intelligence for COVID-19 Diagnosis Through Blood Test Variableshttps://link.springer.com/content/pdf/10.1007/s40313-021-00858-y.pdf
Evaluation of Machine Learning Models for Early Prediction of Gestational Diabetes Using Retrospective Electronic Health Records from Current and Previous Pregnancieshttps://www.medrxiv.org/content/10.1101/2025.05.12.25327431v1.full.pdf
A diagnostic support system based on interpretable machine learning and oscillometry for accurate diagnosis of respiratory dysfunction in silicosishttps://www.biorxiv.org/content/10.1101/2025.01.08.632001v1.full.pdf
Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Datahttps://link.springer.com/chapter/10.1007/978-3-030-93736-2_40
Differentially Private Gradient Boosting on Linear Learners for Tabular Data Analysishttps://assets.amazon.science/fa/3a/a62ba73f4bbda1d880b678c39193/differentially-private-gradient-boosting-on-linear-learners-for-tabular-data-analysis.pdf
Differentially private and explainable boosting machine with enhanced utilityhttps://www.sciencedirect.com/science/article/abs/pii/S0925231224011950
Concrete compressive strength prediction using an explainable boosting machine modelhttps://www.sciencedirect.com/science/article/pii/S2214509523000244/pdfft?md5=171c275b6bcae8897cef03d931e908e2&pid=1-s2.0-S2214509523000244-main.pdf
Assessment of pulse wave velocity through weighted visibility graph metrics from photoplethysmographic signalshttps://www.nature.com/articles/s41598-025-16598-x?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20250826&utm_content=10.1038/s41598-025-16598-x
When Interpretability Meets Generalization: Delta-GAM for Robust Extrapolation in Out-of-Distribution Settingshttps://dl.acm.org/doi/pdf/10.1145/3711896.3737180
Interpretable Prediction of Myocardial Infarction Using Explainable Boosting Machines: A Biomarker-Based Machine Learning Approachhttps://www.mdpi.com/2075-4418/15/17/2219
Using Explainable Machine Learning to Analyse Expert-Guided Automatic Triage Systemshttps://studenttheses.uu.nl/bitstream/handle/20.500.12932/49903/Thomas_vd_Brink_MSc_thesis_2_0-7.pdf?sequence=1&isAllowed=y
Essays on Developing Artificial Intelligence Solutions for Patient centered Healthcare Deliveryhttps://repositories.lib.utexas.edu/server/api/core/bitstreams/35a2df35-efb6-4a2a-951e-c842814afa9d/content
Mitigating Cognitive Biases in Predicting Student Dropout: Global and Local Explainability with Explainable Boosting Machinehttps://www.proquest.com/openview/e4e37e8088593f2db0a9d0e346538ad6/1?pq-origsite=gscholar&cbl=6474026
Proxy endpoints - bridging clinical trials and real world datahttps://pdf.sciencedirectassets.com/272371/1-s2.0-S1532046424X00064/1-s2.0-S1532046424001412/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEIn%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIBYgAN6aOVrDnvQ1932tPndUyJ0Dm1nHdMVLiekPVduQAiAzbYe7W%2Bd6Dj8ee42ZeZnQxJwEjEjuGdiUEPx0a2G43SqyBQgSEAUaDDA1OTAwMzU0Njg2NSIMyMkCUNFeDTCUCppMKo8FiVShykb8phR%2F8aWUGE9gfnE5y7X3Jj1ZA2CVldH13T67s536bdTBhjIMF18rV0YP9iMi6B5aGr%2F286ovIJl332fxZ6iQNBIOPTm8kXQDUqvZbknYldiZqUPs69kuC%2FcKnJd1BWnv2SEZwbRuX94rWnRDPDaSoJx%2FVS6o4qsbFjp9%2BMYZr%2BvJzWHKrXAI4W%2Fh9%2BsIa0yvlac3IMWzAeD23HzDNmF0nqjJ6BSZzmDNW4HRIGBTrTUTO40TzQzhaOY7wyGA0Zv8SpWIULI%2FrY8z8EOX%2FU6OhqgyIMKv%2FSx3rUpMi5CrC1WcpnL97j%2FDAijNi4vMfG1b%2BBQIFRu2EmUky76k4w3FYxkCpYj4n4mk9H%2B%2Bc9C%2BdjKjUiayi%2FisIZUD7ISNhQ9oov0kXI1IVTCGKKQC9jqHOvdiA8YbVuMdEzy1Lkx%2B1kiEo79qvSlpTe2BtWAOm2Iequ01XoaMv%2FQb4ajhWKKSkTafzDAxc58aayP1YH49UzQ68Me7ecdHpx3JUHyYnxJGQ82wRpPkfZJA5wCmOUVI%2FBLuwFJyczG0LpALN5IpIqZz%2B8DvDR0xjRoN49dVwhrTSQ9BesvXbi2LKVm1ptacaaKqyx0PwLjQYKOd%2BPI3zCvRxEiM3IKSNFRLsUTyPNEE4E8pMFNxfyEX59yvTQrHwM62P7hvxHs%2BY6CxUGZTKBQwDAgxttJmiO%2BvjCRbTBXZg1WrQdXCkxntBXb15Mnqxo4lyPzUUkLdLAFK%2BLSwzBIcvSw2qG81Y8qhWmBgBT9vfAoSrjxsILFrB3nnz7u9XNNpRxb5Z9NuNG92%2Fpd%2F%2F5VespMY8Q0iwsNqazZ4M4H8UB34JgtrUEY27WrIsDWzLR%2FAYAxU%2BZHrFzCrsae5BjqyASqDBsNqjEkho%2FbuQDT%2F0vGx%2BgAqrksvVX0GrzNgvqnuPyvw6%2F%2B40ZJP5EA4axfltOYb2tNjd18Ngy2A3cd6J57v1G7wYyuSFIUfHGN5LA8BXK7p0x1mNcwN3pKHtAf260gjpsWMG7anvpK%2F3YupTz498C1lAmurJD%2BLN41lq05wBr403cchE41yzqAKHVKVpNq9s6oGHJmq0KJRvk%2FfjZr8oLhod5gtrwLKvLGqULf50L0%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20241105T092058Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYUKUJCDYI%2F20241105%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=15f8e40964e2c750ae15e43aa8e7f7c76eef6a76b792e41434d14bed42b31432&hash=d4a3e49b29443e5eea9e5a44c0dc11b3f30b21addbe6d6d20d523c68db23cd23&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1532046424001412&tid=spdf-4fbabbd8-becb-4526-98d3-c7517914e457&sid=8ab2a095350fc74edc4b8765ecd8c0260edcgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0f165f0b050207505b0151&rr=8ddbc55a0d60a380&cc=us
Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentationshttps://onlinelibrary.wiley.com/doi/epdf/10.1111/inm.13402
Interpretable Machine Learning Models for Predicting Perioperative Myocardial Injury in Non-Cardiac Surgeryhttps://download.ssrn.com/lancet/3c1e4bc6-9a96-41fe-b60d-4f8c370f9c36-meca.pdf?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEFEaCXVzLWVhc3QtMSJGMEQCIHigbeDDkdVUkffH2H2RZYNuuzMq%2FYlkF%2FibCUOmRXZjAiBJ4I7%2BbMuVoMl4APz5MJ9nmLt%2B73WKABFtE2NSd4azJSrGBQiJ%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAQaDDMwODQ3NTMwMTI1NyIMIVo5lL%2F4nJco3bcLKpoF6PnBkOsbtAPeb6MtFrf4OmMuVjZzVV1Qc6zzBgUTuvijpQFfxzIktRbNW5mUP4fi4rZmjJqhQAjjheSkE7HupPdoBsG3E%2Fii8HKrOXOWErX0qO%2Bwoy%2FFDWS8MdwihFT6EdpwF2yXXAGNz8Vvm%2BgCD772tijc8fXl9DjVtJfr%2BSAPvP3LiG2Pb8UV038Nvko2RbXdyF9rtOmQt1FU738wZ20X3PoLaxzplsAuClhl%2B9zzB5jpC%2BfaW%2BouvPfaZ33McYgKwjTsVv9ox9dBmPgxKbfC%2F5VcF1AAppVeWbGHOj%2BaFIRMocNSaz2IFv06tAqOk7UlIp3p9u4VP8dZ77hf4NI94UEZYojH%2FL%2F6Y23RPj7KtFPjCrHiRbdjaO%2Fl80SoTl21rd41HMBtH%2F%2B%2Fsc5Hgphz9QfI69n03iQ0D6vx30p0knKdwnghrJYYOyW4yCkyztwyYrcOsTOZOcWdvnoGFe6bvIi4DxPrEnf%2FyKDH5iWNeBx5ZAFxzi1ibP8NAXY8c%2B6rr%2Bpe8VGG4shAkI1JRae%2BakvlbBQYbj8vAuVUAGVqM1u8lo0%2FeGfW89KrlojE1VLBUidGrIZSFjCLJP0cG%2FDUbOs7atV1X1H8ny4F5wIw9g0h4lPjj%2FB4vjYPhw%2BOcZYSWiwn1dI0UGNJTyD%2FRtVjuGHT19MOIoqpjSXBymTmOVFjYPSrBYKUUYVfbmzTlrODFwL3oxQwA5eJvAUs0XjXD1rqHQ%2FfISGck5RmCrNlMwpirxgKarRIFwDbzVX8Z7EjTdvIWNAoJJDhwNQCfkdpo12QoINi8yZuiEzdVwMka2sRfIDSyKcn3z2%2B2T6KLgtVf8Q%2BhecxbHgIy8dvcf8secsrh5SDlTia5np%2BNDGLsStb54VQ3oOXMJbE0cQGOrIBoW0oHhskfPiIYI34pCU9cirMG0P9uUmNXP0aJczLXK0mPViF2dAlTOu2uIfl6bFLewWrg7ESDste8Gx6Qci8o4d6rK7ROJv1UpXh9xPRfOx3dksHGKkcW%2FlE4xxEtQ2ctZOSAycoEpPplq4tLgW6BIuDBVR2VxIjY1o5oN7loo13exU4hkJpketet9gRQHdzFYHJYWTzpXDys2TBAp587w2ueQEbnh6zC8GJuv1DxH4OVw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20250807T093300Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAUPUUPRWEUFEGH3IY%2F20250807%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=1ebccc377c0fbab66bb37e6aac8ab4b50cb82328871b5e8d2b651370d49a58e6&abstractId=5379891
Towards Cleaner Cities: Estimating Vehicle-Induced PM2.5 with Hybrid EBM-CMA-ES Modelinghttps://www.mdpi.com/2305-6304/12/11/827
Predicting Robotic Hysterectomy Incision Time: Optimizing Surgical Scheduling with Machine Learninghttps://pmc.ncbi.nlm.nih.gov/articles/PMC11741200/pdf/e2024.00040.pdf
Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cysthttps://pdf.sciencedirectassets.com/282081/1-s2.0-S1424390324X00083/1-s2.0-S1424390324007300/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEE8aCXVzLWVhc3QtMSJIMEYCIQDt5kx%2Fzkij3hPAFFQY1mbWqUbYxcSWI9LWHoRYlmJdiQIhAL6gcQnpJNJHHoYFVmnCSbm7lfZt7oW%2BFLqgmTeaRf0VKrIFCHgQBRoMMDU5MDAzNTQ2ODY1IgwhQHpvAtTg383who4qjwVCnFGFYQ201j6uvYXVvBOdbRZ4f%2FXbMAoapXTH0luD4mA%2BIicn2siAAgpx3%2BKLlcBUkXU8fQ8nda7bp%2FaPM1Lvh%2FKTc29pnSN%2FbJZ00cTPwzE8SUiUhOs5lFwkoCshaq2pGcZ75kOGyj4OUVNqKc6FxzGeu76pkEpvAhbNGytfklu4XQ1H2OwLWvYSM4iPc4AnA9ceQOuu5vTHDapjIfNM90MT9gklreIXXl8jRsnZI0kPIPu%2FPSlQVyA9ovw0q32mrHqq2ms%2Fq%2FT4Y3S%2FXUjsvxA8tJD%2FMcGHh6KziR6RPoBuxa8vRxqRlA500bxo1LxGebC38Yz2vBVFppgBUcrQ%2BiIjDno12m1t2ygD6V9XZWJ45fRNqqOQuOpK7Ayu%2FzVJGS2aUVaziorSYwChpwpd7c39M7B0CieLT7Qsb9Svj7QNuA5iL2i3OlTzfwxBHHyRqWpZRl3wyxFwZKaGsPvf7iMWBfczH9UIXFsQietRs8OKE01sn1itTY6O2udVG877TBLcTehR%2FVDoTi4wAK3NqfyXNxVet6Su0l7DvtVQN6IrPQHyQrX6Bz3FOPyiREKrp%2FpnHMLvtb1DjZud4KeM6REwc7BbCgsRNpgPMN5%2FRUYzRnxGA8pBQKpAIlg5oF%2B4iA7raiF3iXdHVs5QnfWL3oaXGKpAChKy6SpPpgZ%2FXQL4BuD3QMwGh3Bvs6JIC1SDfIrpBv4DbMVKNy%2BsnM2DjLrALtso7n8SSATbbnkEICvlGNJDWQW7vqWD5k5ybHUElQO3fWbmgFhRydm%2BXAE0T55PTbGcQltGgGCPEY4OsHbWGlzQ389IkDsjuGNfnHWkZy7B0RsxxaoCNaURaCSPA5qTF7kjr4IvT83c5myHMJ6Qzb0GOrABUl1b4SJKyRQV7x1owREmTbibeuKUpu9uBXuvapSJ51XmzuOTBe2jUCiwH5PJqU0yripIS%2FCw769TIOofSZXvrmWW%2FMUA%2Bv7H%2BwyD0PDZJpcO%2F7YCUoYkOJfdK7%2Fn5Bfup8aDUjTjpGuIFGBplN6oZKkafNWdZK0byZeT%2Bp4tx5lsVTbEmgGzdrHSqUYtmDgdO65vDofxvIrHeoy6NBZju4dWtiYtEvvEM2jnlvRgpPM%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20250217T155221Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYWJXSGAKI%2F20250217%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=b4010ee01020f9d1e5e9f3425bd34a19008e65fae4ca39b07fa521b78dbf031c&hash=364231ff9004818d51efe4b861dd6efc396bcea566c6656024ee6130788182f8&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1424390324007300&tid=spdf-f3cc76e8-7213-42b5-ba0f-55581d548545&sid=ca4435fb2357444d2418c101a5a89ce4be1dgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=10155d535c5701560101&rr=9136f1afbed97c85&cc=us
Using machine learning to assist decision making in the assessment of mental health patients presenting to emergency departmentshttps://journals.sagepub.com/doi/full/10.1177/20552076241287364
Discovering Phenotype-Specific Clinical Markers in Multiple Sclerosishttps://www.researchgate.net/profile/Salvatore-Giugliano/publication/397633958_Discovering_Phenotype-Specific_Clinical_Markers_in_Multiple_Sclerosis/links/691c4765480eb767581c62df/Discovering-Phenotype-Specific-Clinical-Markers-in-Multiple-Sclerosis.pdf
Insider Threat Detection using Explainable Machine Learning Models in Corporate Networkshttps://www.researchgate.net/profile/Vivek-Singh-240/publication/397714182_Insider_Threat_Detection_using_Explainable_Machine_Learning_Models_in_Corporate_Networks/links/691ca5ed480eb767581c8167/Insider-Threat-Detection-using-Explainable-Machine-Learning-Models-in-Corporate-Networks.pdf
Explainable boosting machine for structural health assessment of reinforced concrete beams using crack width measurementshttps://www.sciencedirect.com/science/article/abs/pii/S0926580525006818
Proposing an inherently interpretable machine learning model for shear strength prediction of reinforced concrete beams with stirrupshttps://pdf.sciencedirectassets.com/287527/1-s2.0-S2214509523X00035/1-s2.0-S2214509524005011/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjECUaCXVzLWVhc3QtMSJGMEQCIB0r0KsYBZufOjbCVtUtozwn1QKMdLt2tbbfhuJKjWlXAiB5Dfr7p0yyj%2FSfypTLmjPL8WbjGAB3tRACFjyyqQbbfiq8BQiu%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAUaDDA1OTAwMzU0Njg2NSIMqBpZ2HmN91c%2BJPqpKpAFZtvqQjCScZa4FN%2FeubsPzOk5c%2B58LliO4Zr%2Bn1pm3vtW4I9I1vA29pkhT5was1N3ccPPIm2jNLwJ%2FHiZej7A2SmFv13Ro3sTvhqG%2F6A9Xx70Nx9jOlDPJUmCypKadKp0FGfuhZQuxeN0b%2F1QUUQZG4RpxC%2FXorRRHmb%2FrXcOWBwu4PmLZAkWmTKpncjDI7oj8eh8yBe6%2FA3JkJ14ZyBgR7JnPzR2ZqMdIhvlKoyMn6EnL1Azq2y3qwEMdzSCvz3wH3sT4pClc2vPs6ruQS4CdT3E7BHrf42Q0VnUXWjuy7gt9iRr0vaWR3tD%2FxyrrEKw7XuMHO9L4rQ4Pfn1dhGZ2J8H5ocwJGSh13U5fY6noyaTNViqvHx1oHNMWL03QpkJxmUxYquBWepcDjxEc32V6eGF7Ecm8Vij3s20wdRNcHqxGFKlUCgph48CKUA79iwSGQCkWQh7bq%2FTtowTbSPud7l8xeG1MvfIVy%2B6yzrjqygvPBQs3qkvdoWUrKXe57bhr2jEkKlSdYyp2TJMD6yoYRdTPyFx5xb0KgIt6KQTPmfbqYXkd3FFz3uc0HmWC5NQz6qP9UzNcBhcK8dXo3Dw042pl0HLO1njFaa%2BBfbT89VUVUIqjrAcmHweIl1v7Eyldzr%2BGBXIlsxPO3gPzyPLF2LTggc6dA%2Bswxmgmkv%2B7n5pU5%2F5sxvEhemb%2Fqu%2B8d47O%2Bn6RH8fL4eLGGL2d0dvFvyE7gEwt%2BaU9HsIN0IHqyH5VmaTF5zaKy%2Fn%2BhkF8yGpe5Hq5yNOUGrfQgfyFn4Kqd%2FTVajxIFzk8DEY%2F%2FFtyGJ%2B8BrHV4P%2FYs8R4XcBzPQtyrTuUC1CGmF01Tc2gnnEo4pVPaIjfBk9B%2BXVMc3Mu4Ywy4L%2BsgY6sgFK3hFIXjIfoVjqrIlBvsGYaFiZB1bVKBVy3DRiBgozzYmIVhipN%2FS%2BPok1oETqvYVvLqEVkGcb5W7nUIK16lFgjwDq6ePuxdqSafgOw5jVQroNsDCPRz8B%2F4fg7kv6gs4R9SX7gCaQ2V7L6NxqJDUUqsCMtIYq05Qx43dGByqLoVEz9USpRBmTLQwpGvOmUaGNNwTsCwmt5gRP8UX3CnkwI%2FydxmhrXLEdaUIFVwJbIor9&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240604T221639Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY4E2DAHPF%2F20240604%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=eece32da8855b55208baecc0ce041e79aa03be1c292b58c67ce0215de36cbdb4&hash=46dd1da122f4cea242c6444a811fb16dde5cb8465e88552ac3eaeee97b975e9b&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2214509524005011&tid=spdf-45c1c4d1-dd97-4c0d-a04f-c30843a79e78&sid=1fea53ed2d5cf1443e4a7c4-33f4bf6475e1gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0f155c5f060d565b01055d&rr=88eb49dd2a5f7688&cc=us
A hybrid machine learning approach for predicting fiber-reinforced polymer-concrete interface bond strengthhttps://download.ssrn.com/eaai/e646e179-ec4a-4987-80b5-8d6bbf43ceda-meca.pdf?response-content-disposition=inline&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBMaCXVzLWVhc3QtMSJHMEUCIFVH%2Ba5TT2NOEqgCl7GMhXBXBZWE9VzzcRFT6kYXzdxYAiEA4yvXsrzNQnNq%2BkJRB0rw1d2p35f418pIO%2FT3PHKoZ%2BoqxgUI%2B%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FARAEGgwzMDg0NzUzMDEyNTciDCD0kCrKAqamcwb9LCqaBb4zlqjDhNBhf%2Frbe%2FX3lzSjvS58HiJQtbOHmzaM7putg93e7Wk8nPesoiupTH8uB5ejDC7stGJElRZp5ulT5M6CokoMu82ERn15kMpkgptj3MVEmsY9VTCP%2BCbROJ6v4YcAttOOAEzOc2M6li6o0w4IsF8DNXEIJr%2FJvjB3IDYPkrmpIiHl25h3AzfxPuOF01E2rgucLnY0xTyKGnPBBDZ%2FPtcuqlk2NKun3Q9HbcKj8EPJP%2FPupMW3IQvMnhcdJqqLHXs6wL1P42NTw5vtZO2W5WiEC1CNGDFUTSFRdb9hjhpH4JsYl8X%2BSFT6mZ31K2HTWeuigs5nXp1JN8r8r4O021yiVxHAJ6Chnddr0Z19iM5yOZA4H1EhO1rxxL0VF%2F%2F8Ac3GxuEfkBiug5wuL7aNlBNX6720pYfHH%2FgyrqdU5KSDIp8VYw3KgEij0LkizBHQIoolC48VAEMNc%2F8iWOdZpAVYprhEbABbff8%2BW6c4y1N9vmLTkjZkJtZODpzpQVjrHkL9hAOvmXZocEEN6maRoVJx3DlcTHrfQr8%2BQnPQnmajb5x0FHo44xxBIUt7UB4FOc6beDprle%2F7BO2SNEPLw6rJ9e3WJeVaYch46iqk2tiWFroNHDXlQ73CbzV59AEVtLAR29eIf7uyz%2BU0fOAXG5oAsJyB7YXUjH%2Bh79sxJgBq3%2FoqkEja06CFPRhWeqxixc8y9bEU%2FvvjhfbcWcxGY%2Be%2FwnXbemUbSyr26Y5xvADyicKIMexZNjeHBJ9MKMifQ9oh%2FjmudjxtMLbTpA6EAxMelLjhWcoURF0XeTttMEzEuTjO1OXUwMeXSPZ9roJqH3DB4PHi%2B8UIUG1JoVocv7wDu5ZVlMzgmDr0ti1BShKr9szxagq34jCEkJe8BjqxAbm7bsef33J3AImECx0GZeL0R2tFJZ7ctogL261zP7RqJ4T71rDMbpyfX6HfGuNEbWVROKHUexpuH8FZBodmn%2FjDjZSviK1oxQ1L5TDA2rwMsodnThreIad8vSXqxAzx9qng%2BeN2llXkNdIB7WEnkttzcJ24pZqwYnPI%2FsOznTq%2BDJ88mdNPtzph%2FGdVQcR99tV3waapotTEnUjjoqTTSh9aMgi1jIYMGMrJj6Jb4N%2FhWA%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20250114T024208Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAUPUUPRWEXKDDLJZE%2F20250114%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=11c6f325f84736d5324ab155663c94231696de52be8910a03bb5e9c18f0d1689&abstractId=5055231
Explainable boosting machine for structural health assessment of reinforced concrete beams using crack width measurementshttps://www.sciencedirect.com/science/article/abs/pii/S0926580525006818
Predicting Blood Pressure Variability in Hemodialysis Using an Explainable Boosting Machine Modelhttps://watermark02.silverchair.com/sfaf349.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAA2MwggNfBgkqhkiG9w0BBwagggNQMIIDTAIBADCCA0UGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMm7wA5F_lPXmvNdTqAgEQgIIDFntD2DpLY86ZmHPq_NP8eqetbgsJAQoWdmWJbrOCXQSmzUiPWAF4tXg7y3Zm-pbozGaU__mYTs4vtxjBa-e6SEGBiAwHX_ypi84R2nwLEuxNcnPKP1jIZs68e4Ic9WTH3PHCWt9AgzE-plVFtuZPrkpRbdqXcnKYhuu1yoRW-apB_SDQRiX5ofrY-iIRyXtktYlmMQKCkaPRnUS-p08JMtTYpZJl1REUxZB7-Awfaq364Qlpkunu7FBS4njs6eAr6Gmo2LiUj538WQ4LFrXhoUj4IcbIJRNcoPuyOowdPAGihUtam5SnVONTB5DvWI3zYE-AS8bNXw_1hYI91wk0BbvLg2phvPT1lyOMRJOLVzqQ3cvuyu5yob6tU3ZDLF3c__Dfe9F7RFJ6WxX98V6eqIyuR39ol6tJXgSvIZ59FH26KO2ItaYSDHWwkGxst32eBo0zu0vkNpMF_PVm7X69HT2VLkZyw1Ke-yQHh-h9K58VsqD3kk5E0-ZjGprIHEqyIMG-D22-mS7_6-lVIGoVZAY7tFH7dHpMDu4WacLQ-FiCo_Bq0G_VBlD9b-gzcY-L6b-pXxRaygS3Km3DfS_VTu0iFum3e_NaL8-mf0EOxFWD3J83iTDr135u4nkjwFfG4ee4Yr-TzrNQtue_SqmiGGH0qd6ML8ahL9oSzYZZMAjCHwV_yU9xRSxqm2B7xt6EFOTJ_tLKV-nXV51xFI9p77cVimNGqIYLABdjwNftGWqnhcTRoOIdeVsfQcg7sWO6Tjr37tt9AmaK4AazAjf97l2EI2vkdSiHvKZ9oHVxRzatb-iOUmaI0LG7CiwazcRx6FQqr1BOX-duhDO558Mq8U3Jw47KLK177VzKAE1YpAqKiVPh56bki43EGDHW6cpM53xQe-hqxOQkOk0UkkQMTTdW62r288VtRdvHd6WMG95cwYUPIiQFN4GYedOa2aGrTG2adZkW_CPmzUogmiqY6jcDmySlb6MUuadYMYnYFnjYa4hVl-0MSsl4x9XC-j5f357TUq_ugMVD__tLKHK9YBtuE_dzAME
Validating Explainer Methods: A Functionally GroundedApproach for Numerical Forecastinghttps://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70060
Using explainable machine learning and fitbit data to investigate predictors of adolescent obesityhttps://www.nature.com/articles/s41598-024-60811-2
Interpretable Predictive Value of Including HDL-2b and HDL-3 in an Explainable Boosting Machine Model for Multiclass Classification of Coronary Artery Stenosis Severity in Acute Myocardial Infarction Patientshttps://watermark.silverchair.com/ztae100.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAA2owggNmBgkqhkiG9w0BBwagggNXMIIDUwIBADCCA0wGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMnDqoUBnqG9Zyr0dAAgEQgIIDHT2M3owEzTRAV3KZzrOpzyqOYgClio-CQrzB5731fvsEe9ZWO_QfqQAKdaPyyOsEKjacd25hWs-_OvgXCqc36R4yFWu46PFOCApII2s3hbHYI1XEQozWfdyosgaQf_e7_5RIqIfwTEHt19LoYZuaDYjCqq2vmWOMZb6dNI6mz-h3Zd6BgbyYAFgRHiJfU94NU0Crf_AbbTx2jW3HqMBLYPn-ysUiyQYILNmqlKAAlw81ZjBwzusaQFsiJMCxwGyFHks7nwtnUQ8J5PU5Jelp8_fQ8x5_dlZvzvdkI9MR87zUkk4hm2XL0uyfvH92-7VV_2gMe-rU3aJZhbHJu2hENPDh_OmoDe7SOC-5EwPsgIDoDr_dgSgyhBMIbOk_TrSM4oEN6dbtvfLSDXQUWDV4semLuPjqz7WyiQz4PPt1mXuaf12X5xyVsf1Mms4UpGAKLyoCdJ-zDJ9csOPCefIsV2Bzs-KzaD63HWFLJuCU0hWIaK0QOcJATnpQb1PhFiAF6YZ_cCYTxkuAcrQyHS-WCEefNy8hB8PQXhNljtw0J499qdnLcNOM1gAQ3-o21KaTrEFs-DyvZwWmaGn8Zw1bK1CG8yVxWOh6_wjJpGjMMenstzrKFcLbJADs1yf3PuNGZds0g-Qf4NDcgsturcr0V1nLHVRFazWZhUKSeRnLjPzA5i3lVKnmwKjKa_50i0LMSIXNFS-dmvHs-qVUb8FO0_aKZ6egckXkoGG8w3Jox4MhhY2-B28Z0wbJOj8_DojCCtAmAPC0T5emRsuk1rkuRXIoMtFDWN0l7fr7RVkuy1TEd3mpa5UuU7Qo-wu_yqi6ibwLupjGeVN__7SeteoBSh8yFJgYN4BEiYmdkEX7DgKaMC90h5GakNJ7zeAPR9PFnQVRORoof04qMWK4aGod2igso1-qsCup-kVWmPy8zrQKlqxE4OCeqUpKQgZMUUAlFu643iuRnQuLnahXhui45TY8lS56XGCLqkwSG594lMoAXAYZ9tVFM4fAVwQJ3EWkJfHRRCWWGZfLwBPsdUnNEziGg4QIdrKhe-Fu7nLF
Estimate Deformation Capacity of Non-Ductile RC Shear Walls Using Explainable Boosting Machinehttps://arxiv.org/pdf/2301.04652.pdf
Introducing the Rank-Biased Overlap as Similarity Measure for Feature Importance in Explainable Machine Learning: A Case Study on Parkinson’s Diseasehttps://www.researchgate.net/publication/362808061_Introducing_the_Rank-Biased_Overlap_as_Similarity_Measure_for_Feature_Importance_in_Explainable_Machine_Learning_A_Case_Study_on_Parkinson's_Disease
Targeting resources efficiently and justifiably by combining causal machine learning and theoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768181/pdf/frai-05-1015604.pdf
Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selectionhttps://arxiv.org/pdf/2212.10707.pdf
Death by Round Numbers: Glass-Box Machine Learning Uncovers Biases in Medical Practicehttps://www.medrxiv.org/content/medrxiv/early/2022/11/28/2022.04.30.22274520.full.pdf
Post-Hoc Interpretation of Transformer Hyperparameters with Explainable Boosting Machineshttps://www.cs.jhu.edu/~xzhan138/papers/BLACK2022.pdf
Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinomahttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9771385/pdf/frai-05-1059033.pdf
Exploring the Balance between Interpretability and Performance with carefully designed Constrainable Neural Additive Modelshttps://www.sciencedirect.com/science/article/pii/S1566253523001987
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable MLhttps://arxiv.org/pdf/2211.08991.pdf
StratoMod: Predicting sequencing and variant calling errors with interpretable machine learninghttps://www.biorxiv.org/content/10.1101/2023.01.20.524401v1.full.pdf
Interpretable machine learning algorithms to predict leaf senescence date of deciduous treeshttps://pdf.sciencedirectassets.com/271723/1-s2.0-S0168192323X00112/1-s2.0-S0168192323003143/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEOP%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIArPrCug2%2BpvA%2F87dfMYdbINsntWDDgNHeCOn72Yfad3AiBHzR9BvMkRvZrjQZ1DoY1YMkD6VsQw45zqo5ykkClnHSq8BQiL%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAUaDDA1OTAwMzU0Njg2NSIMdV4IhgM83azwHKyjKpAFFMABPkhGjjH1i3y26weF5LN6ZuxfgcDlklmnpEZDoEntreay08vlEU7%2F3CLeNSqYgaq5txCiVztJDv2TBcxDUt0PP4faNrHUWIQdfDksvDs3EE7VEupaqhVjMNi0%2F%2ByLRw2OzjMPpz7H5sd3i4%2F%2FK2%2FJlpAWHlr4RFJ9BXMMPbLDEqhjIJIl5ZzaeLeeijXKrTtvJ6iYwTic%2FHJ23m7Fdnkh94HKkFTOWeglJzGT7FSc5Wnc7DgExrL7EBLvu9YVusMUf9rFYIU%2BKaVyxIa7WDUN48cWjwdGLjYV9XPy%2FP2lRKjeiiNMYbdknQzJfSzh0HWxx0Aq6zlXdkJUbvSgqFoDC2npaUGXjNupSLNIzcMFWr8lUvUFIBm1ZigETFDZrB4zEJFQVxXV%2Bsztpcs1tMO%2F8LAG3MNI%2BI%2Fp7lT3bj%2F%2BZg6S7d6ROGS96XMS3Am3WffiwNIxueTGrWmRWxS75EQexcJmrQ4ELU%2By3vOXxIvqftT68w6%2BnBryUB5kGE%2B6GljxUFD5y7hZFLM0tfFW9XEZF5PjDbz%2Fx%2Bi0dxEiwvN2mzNpSAWiiy6ZBT31GSRRMtTe9Sm4U%2B8DwSR0fymXmme5fKLGzkySq0xPuFhzN6LyLCoxtbob%2BRyLALNdP8E31enPu%2B1xl5Isg%2FXHINRM29SYzK0u1PlPK78ng%2Bqt4mUlLD7jlzIeBKa1vz%2BU8%2F1ZYvEofc8i6q691PqjYl%2FZK5lFQO1EEremVOv4i2nEYwmGtjtCAk1WFChnamFlEdWyJIerN5pKI4YvsGF%2FwXG8aHuYBg41CfGftl%2FwlJ77dPOQ8QHgp5BZFheyeYwEMijnbz4terE7kVpdvBKOk5lBxtiJILI0ftU%2F4F0k825M%2Ft4w%2FqzIpgY6sgFspzJ6vfwqmIKbmprTCY6NBr4uAZU%2FPUWraWxu3hCydMZTVOjlrab%2Bv5NSdCqWKHvK7Yn89JtE9um3P8Gyev9BFPXT6LykCtjNOulKUQnywvl8ngKdbujNjLAyZb4D0p4dFRFsE2sUTUWNvs%2BVwA%2BYdn4%2BwPkMN5PU0KR78myJ7LyYJGodNLOXcBSV%2FXa396TmeXagW3ihm2U7H%2FvXm1IZmOz%2FflT5y6CEy%2FegChXEVpb6&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230808T111525Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY64PTFOFS%2F20230808%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=e35040e1985923b74081dbdac33f7250949695d95e631d68a8fe20684b3746bc&hash=59ce65176ba4b931ecc905ef2a0bb80561947d73205e8ad2561d63a95552a4fb&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0168192323003143&tid=spdf-41137a89-2992-4585-8512-4303f8dedb0c&sid=b0b6f2a791aeb640d1897e968c8092375869gxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=10145807525053555255&rr=7f375764f8ea2338&cc=us
Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Studyhttps://cardio.jmir.org/2023/1/e47736/PDF
An Explainable AI Approach using Graph Learning to Predict ICU Length of Stayhttps://shichangzh.github.io/preprints/LoS_XAI_ISR.pdf
Cross Feature Selection to Eliminate Spurious Interactions and Single Feature Dominance Explainable Boosting Machineshttps://arxiv.org/ftp/arxiv/papers/2307/2307.08485.pdf
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Modelshttps://arxiv.org/pdf/2307.08175v1.pdf
An explainable model to support the decision about the therapy protocol for AMLhttps://arxiv.org/pdf/2307.02631.pdf
Assessing wind field characteristics along the airport runway glide slope: an explainable boosting machine-assisted wind tunnel studyhttps://www.nature.com/articles/s41598-023-36495-5
Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracyhttps://arxiv.org/pdf/2310.18629
Trustworthy Academic Risk Prediction with Explainable Boosting Machineshttps://link.springer.com/chapter/10.1007/978-3-031-36272-9_38
Binary ECG Classification Using Explainable Boosting Machines for IoT Edge Deviceshttps://ieeexplore.ieee.org/document/9970834
Explainable artificial intelligence toward usable and trustworthy computer-aided diagnosis of multiple sclerosis from Optical Coherence Tomographyhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406231/
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer’s Diseasehttps://arxiv.org/pdf/2308.07778.pdf
Prediction of Alzheimer Disease on the DARWIN Dataset with Dimensionality Reduction and Explainability Techniqueshttps://www.scitepress.org/Papers/2024/130174/130174.pdf
Explainable Boosting Machine for Predicting Alzheimer’s Disease from MRI Hippocampal Subfieldshttps://link.springer.com/chapter/10.1007/978-3-030-86993-9_31
Comparing explainable machine learning approaches with traditional statistical methods for evaluating stroke risk models: retrospective cohort studyhttps://pureadmin.qub.ac.uk/ws/portalfiles/portal/495863198/JMIR_Cardio.pdf
Explainable Artificial Intelligence for Cotton Yield Prediction With Multisource Datahttps://ieeexplore.ieee.org/document/10214067
Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizinghttps://insightsimaging.springeropen.com/articles/10.1186/s13244-024-01876-5
Monotone Tree-Based GAMI Models by Adapting XGBoosthttps://arxiv.org/ftp/arxiv/papers/2309/2309.02426.pdf
Neural Graphical Modelshttps://arxiv.org/pdf/2210.00453.pdf
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent MLhttps://arxiv.org/pdf/2402.12630v1.pdf
The Quantitative Analysis of Explainable AI for Network Anomaly Detectionhttps://studenttheses.uu.nl/bitstream/handle/20.500.12932/45996/Thesis_SinievanderBen_6021794.pdf?sequence=1&isAllowed=y
Enhancing Predictive Battery Maintenance Through the Use of Explainable Boosting Machinehttps://link.springer.com/chapter/10.1007/978-3-031-44146-2_6
Improved Differentially Private Regression via Gradient Boostinghttps://arxiv.org/pdf/2303.03451.pdf
Explainable Artificial Intelligence in Job Recommendation Systemshttp://essay.utwente.nl/96974/1/Tran_MA_EEMCS.pdf
Diagnosis uncertain models for medical risk predictionhttps://arxiv.org/pdf/2306.17337.pdf
Extending Explainable Boosting Machines to Scientific Image Datahttps://arxiv.org/pdf/2305.16526.pdf
Exploring explanation deficits in subclinical mastitis detection with explainable boosting machineshttps://link.springer.com/article/10.1007/s44279-025-00246-z
Pest Presence Prediction Using Interpretable Machine Learninghttps://arxiv.org/pdf/2205.07723.pdf
Key Thresholds and Relative Contributions of Knee Geometry, Anteroposterior Laxity, and Body Weight as Risk Factors for Noncontact ACL Injuryhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10184233/pdf/10.1177_23259671231163627.pdf
A clinical prediction model for 10-year risk of self-reported osteoporosis diagnosis in pre- and perimenopausal womenhttps://pubmed.ncbi.nlm.nih.gov/37273115/
epitope1D: Accurate Taxonomy-Aware B-Cell Linear Epitope Predictionhttps://www.biorxiv.org/content/10.1101/2022.10.17.512613v1.full.pdf
Explainable Boosting Machines for Slope Failure Spatial Predictive Modelinghttps://www.mdpi.com/2072-4292/13/24/4991/htm
Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Healthhttps://arxiv.org/pdf/2109.13770.pdf
Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19https://www.medrxiv.org/content/10.1101/2020.06.30.20143651v1.full.pdf
Leveraging interpretable machine learning in intensive carehttps://link.springer.com/article/10.1007/s10479-024-06226-8#Tab10
Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretabilityhttps://www.pure.ed.ac.uk/ws/portalfiles/portal/343114800/1_s2.0_S0169260723001487_main.pdf
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomeshttps://arxiv.org/pdf/2207.05322.pdf
Calibrate: Interactive Analysis of Probabilistic Model Outputhttps://arxiv.org/pdf/2207.13770.pdf
Neural Additive Models: Interpretable Machine Learning with Neural Netshttps://arxiv.org/pdf/2004.13912.pdf
TabSRA: An Attention based Self-Explainable Model for Tabular Learninghttps://www.esann.org/sites/default/files/proceedings/2023/ES2023-37.pdf
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detectionhttps://arxiv.org/pdf/2409.10111v1
Improving Neural Additive Models with Bayesian Principleshttps://arxiv.org/pdf/2305.16905.pdf
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learninghttps://arxiv.org/pdf/2106.01613.pdf
Scalable Interpretability via Polynomialshttps://arxiv.org/pdf/2205.14108v1.pdf
Polynomial Threshold Functions of Bounded Tree-Width: Some Explainability and Complexity Aspectshttps://arxiv.org/pdf/2501.08297
Neural Basis Models for Interpretabilityhttps://arxiv.org/pdf/2205.14120.pdf
ILMART: Interpretable Ranking with Constrained LambdaMARThttps://arxiv.org/pdf/2206.00473.pdf
Integrating Co-Clustering and Interpretable Machine Learning for the Prediction of Intravenous Immunoglobulin Resistance in Kawasaki Diseasehttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9097874
Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regressionhttps://arxiv.org/pdf/2403.14328
Proxy Endpoints - Bridging clinical trials and real world datahttps://deliverypdf.ssrn.com/delivery.php?ID=100104064008112111075114086019087126028049030043069035029115016108019006060084089121082084037060084007106031067094003062092094027085086025068093071031052079088007024075059029108100000124020112107075035009017105116086086122095064020024067066064103085015070113092118127102118080007103101&EXT=pdf&INDEX=TRUE
Application of boosted trees to the prognosis prediction of COVID‐19https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11111612/pdf/HSR2-7-e2104.pdf
Explainable Gradient Boosting for Corporate Crisis Forecasting in Italian Businesseshttps://assets-eu.researchsquare.com/files/rs-4426436/v1_covered_0583163e-fa83-4b34-9a7e-eae573b17bd8.pdf?c=1715832940
Revisiting differentially private XGBoost: Are random decision trees really better than greedy ones?https://openreview.net/pdf?id=bCynxWndWY
Investigating Trust in Human-Machine Learning Collaboration: A Pilot Study on Estimating Public Anxiety from Speechhttps://dl.acm.org/doi/pdf/10.1145/3462244.3479926
pureGAM: Learning an Inherently Pure Additive Modelhttps://www.microsoft.com/en-us/research/uploads/prod/2022/07/pureGAM-camera-ready.pdf
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactionshttps://arxiv.org/pdf/2003.07132v1.pdf
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisonshttps://arxiv.org/ftp/arxiv/papers/2305/2305.15670.pdf
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Modelshttps://arxiv.org/ftp/arxiv/papers/2207/2207.06950.pdf
Interpretable generalized additive neural networkshttps://pdf.sciencedirectassets.com/271700/AIP/1-s2.0-S0377221723005027/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEOX%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJHMEUCIQDYA80LSoQY%2FmGTGsi8cQ2BzHoFU7410ljuYQwqt9ht0gIgBg4NSEN4e5jKUouf04uZCPIMh8NHH22jrY3opOG%2Fa1wqsgUIXhAFGgwwNTkwMDM1NDY4NjUiDFuCZlUMsD25uY5h3CqPBW0KcZyo1I0j19n0O26WHoCoxeimG0I7m02rUpQug4EiDYFVkx%2FRqfC4eL2Y0z7iO%2B95NIQ9UrOd3zWWZZPGpKCgHpU1GA4JwHSKNJDi8G2q%2FGm18%2Fl8B9jN4Lq3klUfU3HcjJh%2B4O1aZTJb3PmqDxKn%2BFQIftfS13xNcyqGnGBlw3yaSp3ZXoV55tKSX6b%2Fp5ZuXWORWiC2JlANxa0exR%2FkBeE75gfILdU8bH2TJ1wozoB0yTZwDAl1%2Bc4exGhVdhZRpvr9W6q%2BTG4tx6qhglAwv1uUQN8Zt1z8GEFHMTrtSv5pNJIpLqqMxp62UeufPMesYyoO5RfKjRS96PxYs1S%2FC5zfz0V63kkFtmSVn4IzVQ%2B9tLq%2FEWQ3BvTs8B0cH%2FOm6W8wn4nGk3HywJiUWvGexXahMqDW9o2pq7CWOSoFCKjjkOyxBXAzP0OX5LeCCgOF11BbhNcDSiIqlWQhqsk0738appUu99Yh12XmMWyu6YXAv1jvgrpaliMRkliAu9by418e6%2FBBA%2B%2BfcDQC3VkEv3NpSQklitMaIT2Y624jhM09ntjdC4IcONNRVE3Q5sHIh6DZsBHrPj9oKqpu9nPKnDBrKoAFdnQ%2BkLQ%2B8JAXyCHwd3YBUXQStlYTpUExESOnFFJ36HGJ%2FbkkFC5Ac9W%2BALq%2FkBYIvtPFNBWIGUSC%2BUgSH0kC%2BJqoyYUNHjfYZ3fxCDwI%2BAugNT3UtXtT%2BrnCKlH3f68ZAyOdkFLiHRQevc2%2FRBXJ5gAqCgZFDUVM%2BVjgB%2BInE458PRMxLuRwFHJarOqZhoDvC68ar2q3YDPyqmyUZxaiVrqn2xlJGdh0lcTVwNourzqlY2l4v87nYm7ncxJO%2FiiBQArtSRTOWmkw6JigpQY6sQEhgDdw23Gwj9rSPFlCHfUzj%2B%2BfdgeX3LZpuPITkl6%2BYwjKw0wXpR4c0Rj0IsCH1EAxJcxSLXhSSHgInlZR41EreEAByudeYNtxD0iAQcR3L4RlqTVuI6V3IIcxNltdg5rDAJwUqsGqhMrZOH0uJqQXvLJwxgfkOkckdjdnrfT%2FmOh%2BNjLCR4KTvwTJIC2YAmNHQco2TLKbC27i118DSoKwrYUvb%2BkCTwD3TMkxIf%2BrW5s%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230707T133754Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYTYKBXRE7%2F20230707%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=389ecd144af85f4eae42ab9684f9d56696191a9d8d33c44386ee6af520187724&hash=e798cbd4d80d01d56a2a1ea75a3947b027daecaea5f6e6674a1dc2dbea97dab3&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0377221723005027&tid=spdf-79e45837-627e-4a9f-88f5-7359ecb4ca63&sid=7e54333b754ff04056483e557e54be0269ddgxrqa&type=client&tsoh=d3d3LnNjaWVuY2VkaXJlY3QuY29t&ua=0b1a5101565e5607565a&rr=7e307c188e87e7cb&cc=mx
A Concept and Argumentation based Interpretable Model in High Risk Domainshttps://arxiv.org/pdf/2208.08149.pdf
Analyzing the Differences between Professional and Amateur Esports through Win Probabilityhttps://dl.acm.org/doi/pdf/10.1145/3485447.3512277
Explainable machine learning with pairwise interactions for the classification of Parkinson’s disease and SWEDD from clinical and imaging featureshttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132761/pdf/11682_2022_Article_688.pdf
Interpretable Prediction of Goals in Soccerhttps://statsbomb.com/wp-content/uploads/2019/10/decroos-interpretability-statsbomb.pdf
Extending the Tsetlin Machine with Integer-Weighted Clauses for Increased Interpretabilityhttps://arxiv.org/pdf/2005.05131.pdf
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Predictionhttps://arxiv.org/pdf/2005.04176.pdf
From Shapley Values to Generalized Additive Models and backhttps://arxiv.org/pdf/2209.04012.pdf
Developing A Visual-Interactive Interface for Electronic Health Record Labelinghttps://arxiv.org/pdf/2209.12778.pdf
Development and Validation of an Interpretable 3-day Intensive Care Unit Readmission Prediction Model Using Explainable Boosting Machineshttps://www.medrxiv.org/content/10.1101/2021.11.01.21265700v1.full.pdf
EPS: An Explainable Post-Shot Expected Goal Metric for Evaluating Goalkeepers and Attackershttps://hal.science/hal-05258651/
Prediction of surface rougness of additively manufactured and machined parts via machine learninghttps://open.metu.edu.tr/handle/11511/115643
Development of Explainable Machine Learning Models to Predict Outcomes After Platelet-Rich Plasma Injections for Knee Osteoarthritishttps://journals.sagepub.com/doi/full/10.1177/23259671251349743
Transparent Machine Learning: Training and Refining an Explainable Boosting Machine to Identify Overshooting Tops in Satellite Imageryhttps://arxiv.org/pdf/2507.03183
Death by Round Numbers and Sharp Thresholds: How to Avoid Dangerous AI EHR Recommendationshttps://www.medrxiv.org/content/10.1101/2022.04.30.22274520v1.full.pdf
Building a predictive model to identify clinical indicators for COVID-19 using machine learning methodhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9037972/pdf/11517_2022_Article_2568.pdf
Using Innovative Machine Learning Methods to Screen and Identify Predictors of Congenital Heart Diseaseshttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8777022/pdf/fcvm-08-797002.pdf
Impact of Accuracy on Model Interpretationshttps://arxiv.org/pdf/2011.09903.pdf
Machine Learning Algorithms for Identifying Dependencies in OT Protocolshttps://www.mdpi.com/1996-1073/16/10/4056
Causal Understanding of Why Users Share Hate Speech on Social Mediahttps://arxiv.org/pdf/2310.15772.pdf
Explainable Boosting Machine: A Contemporary Glass-Box Model to Analyze Work Zone-Related Road Traffic Crasheshttps://www.mdpi.com/2313-576X/9/4/83
Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machineshttps://arxiv.org/pdf/2402.03457.pdf
Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabeticshttps://www.mdpi.com/2218-1989/13/12/1204
A proposed tree-based explainable artificial intelligence approach for the prediction of angina pectorishttps://www.nature.com/articles/s41598-023-49673-2
Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Datahttps://www.mdpi.com/2073-4433/15/1/20
On the Physical Nature of Lya Transmission Spikes in High Redshift Quasar Spectrahttps://arxiv.org/pdf/2401.04762.pdf
GRAND-SLAMIN’ Interpretable Additive Modeling with Structural Constraintshttps://openreview.net/pdf?id=F5DYsAc7Rt
Identification of groundwater potential zones in data-scarce mountainous region using explainable machine learninghttps://www.sciencedirect.com/science/article/pii/S0022169423013598
Explainable Classification Techniques for Quantum Dot Device Measurementshttps://arxiv.org/pdf/2402.13699v1.pdf
https://patch-diff.githubusercontent.com/interpretml/interpret#books-that-cover-ebms
Machine Learning for High-Risk Applicationshttps://www.oreilly.com/library/view/machine-learning-for/9781098102425/
Explainable AI with Pythonhttps://www.amazon.com/Explainable-AI-Python-Antonio-Cecco/dp/303192228X/ref=sr_1_1?crid=1D8M7T61VP2N9&dib=eyJ2IjoiMSJ9.Yuoa4g8YohqY3C8Umi1E0qOZfUgATZQR2GpBnxVUHuwvH1ml3gkgioEHWOHqod3AQtc8jPXxX6KvFVjsjuN3oZUzw8k5XALdGIsx8bk6ZCT4yRNEX8vgJHjhf7oVFRdbSvsRAWcDem2GI4I8F0xawxpgxYvpNDCzk9J8-FmIxeMN0tMST89WbTVTNO30crxXj5gok1PuZV_1IxYa6tyVhmtGlesWHcewNG0h7wfMIbk.FAlg6RW_KFog7rTG2O5z0425E88Z20EHAqSM2deROyI&dib_tag=se&keywords=Explainable+AI+with+Python&qid=1754559323&sprefix=%2Caps%2C147&sr=8-1
Interpretable Machine Learning with Pythonhttps://www.amazon.com/Interpretable-Machine-Learning-Python-hands-dp-180323542X/dp/180323542X/
Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learninghttps://www.amazon.com/Explainable-Artificial-Intelligence_-An-Introduction-to-Interpretable-XAI/dp/3030833550
Applied Machine Learning Explainability Techniqueshttps://www.amazon.com/Applied-Machine-Learning-Explainability-Techniques/dp/1803246154
The eXplainable A.I.: With Python exampleshttps://www.amazon.com/eXplainable-I-Python-examples-ebook/dp/B0B4F98MN6
Platform and Model Design for Responsible AI: Design and build resilient, private, fair, and transparent machine learning modelshttps://www.amazon.com/Platform-Model-Design-Responsible-transparent/dp/1803237074
Explainable AI Recipeshttps://www.amazon.com/Explainable-Recipes-Implement-Explainability-Interpretability-ebook/dp/B0BSF5NBY7
Ensemble Methods for Machine Learninghttps://www.amazon.com/Ensemble-Methods-Machine-Learning-Kunapuli/dp/1617297135
Interpretability and Explainability in AI Using Pythonhttps://www.amazon.com/Interpretability-Explainability-Using-Python-Decision-Making/dp/B0F536GGT5/ref=sr_1_1?crid=5QJBMJKZOJ4H&dib=eyJ2IjoiMSJ9.oiAm3_DaQcHqA3YNRGrC70d1KcpeDZReI29ATLUdCe0VWb6wKLo-U1iLlyW24-u0SIdRxce8m_E1urP9pl-Qwjm9JSfu6l8nX3Ws9itlpXw.AJkX9wz_VBTb3OSeiW22Fbt2NCI3_kM7zJ_TCTUcbt0&dib_tag=se&keywords=interpretability+and+explainability+in+ai&qid=1748138569&sprefix=interpretability+and+explainability+in+ai%2Caps%2C175&sr=8-1
https://patch-diff.githubusercontent.com/interpretml/interpret#external-tools
R package for building EBMs through reticulatehttps://github.com/bgreenwell/ebm
EBM to Onnx converter by SoftAtHomehttps://github.com/interpretml/ebm2onnx
EBM to SQL converter - ML 2 SQLhttps://github.com/kaspersgit/ml_2_sql
EBM to PMML converter - SkLearn2PMMLhttps://github.com/jpmml/sklearn2pmml
EBM visual editor - GAM Changerhttps://github.com/interpretml/gam-changer
Interpreting Visual Clusters in Dimensionality Reduction - DimVishttps://github.com/parisa-salmanian/DimVis
https://patch-diff.githubusercontent.com/interpretml/interpret#contact-us
https://patch-diff.githubusercontent.com/interpretml/interpret#if-a-tree-fell-in-your-random-forest-would-anyone-notice
interpret.ml/docshttps://interpret.ml/docs
machine-learning https://patch-diff.githubusercontent.com/topics/machine-learning
ai https://patch-diff.githubusercontent.com/topics/ai
scikit-learn https://patch-diff.githubusercontent.com/topics/scikit-learn
artificial-intelligence https://patch-diff.githubusercontent.com/topics/artificial-intelligence
transparency https://patch-diff.githubusercontent.com/topics/transparency
blackbox https://patch-diff.githubusercontent.com/topics/blackbox
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