Cover Image

Are machine learning interpretations reliable? A stability study on global interpretations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Gan, Luqin, Zikry, Tarek M., Allen, Genevera I.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Applications
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Group-Aware Matrix Estimation and Latent Subspace Recovery
by: Golubovic, Hamza, et al.
Published: (2026)

Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
by: Chang, Andersen, et al.
Published: (2025)

LOCO Feature Importance Inference without Data Splitting via Minipatch Ensembles
by: Gan, Luqin, et al.
Published: (2022)

Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuation
by: Tanashkin, Alexey S., et al.
Published: (2025)

Modelling higher education dropouts using sparse and interpretable post-clustering logistic regression
by: Nigri, Andrea, et al.
Published: (2025)

Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets
by: Xia, Ji, et al.
Published: (2025)

Fast and interpretable electricity consumption scenario generation for individual consumers
by: Soenen, J., et al.
Published: (2024)

Classification problem in liability insurance using machine learning models: a comparative study
by: Qazvini, Marjan
Published: (2024)

Driving pattern interpretation based on action phases clustering
by: Yao, Xue, et al.
Published: (2024)

Causal machine learning for predicting treatment outcomes
by: Feuerriegel, Stefan, et al.
Published: (2024)

Two-step interpretable modeling of Intensive Care Acquired Infections
by: Lancia, Giacomo, et al.
Published: (2023)

Applications of machine learning to predict seasonal precipitation for East Africa
by: Scheuerer, Michael, et al.
Published: (2024)

Probabilistic intraday electricity price forecasting using generative machine learning
by: Chen, Jieyu, et al.
Published: (2025)

Error-controlled non-additive interaction discovery in machine learning models
by: Chen, Winston, et al.
Published: (2024)

Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors
by: González-Castro, Ana, et al.
Published: (2025)

A two-step machine learning approach to statistical post-processing of weather forecasts for power generation
by: Baran, Ágnes, et al.
Published: (2022)

Analysis of ELSA COVID-19 Substudy response rate using machine learning algorithms
by: Qazvini, Marjan
Published: (2024)

Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
by: Zhou, Ya, et al.
Published: (2026)

A comparison between geostatistical and machine learning models for spatio-temporal prediction of PM2.5 data
by: Mohamed, Zeinab, et al.
Published: (2025)

Variable importance measure for spatial machine learning models with application to air pollution exposure prediction
by: Cheng, Si, et al.
Published: (2024)

Tackling water table depth modeling via machine learning: From proxy observations to verifiability
by: Janssen, Joseph, et al.
Published: (2024)

Bayesian sparse modeling for interpretable prediction of hydroxide ion conductivity in anion-conductive polymer membranes
by: Murakami, Ryo, et al.
Published: (2025)

Predicting loss-of-function impact of genetic mutations: a machine learning approach
by: Kaur, Arshmeet, et al.
Published: (2024)

Stability of clinical prediction models developed using statistical or machine learning methods
by: Riley, Richard D, et al.
Published: (2022)

A comparative analysis of machine learning algorithms for predicting probabilities of default
by: Cristescu, Adrian Iulian, et al.
Published: (2025)

Beyond Beats: A Recipe to Song Popularity? A machine learning approach
by: Sebastian, Niklas, et al.
Published: (2024)

Density correction for multivariate spatial fields of global climate model output using deep learning
by: Majumder, Reetam, et al.
Published: (2024)

A meta-analysis on the performance of machine-learning based language models for sentiment analysis
by: Rohde, Elena, et al.
Published: (2025)

A generative machine learning model for designing metal hydrides applied to hydrogen storage
by: Liu, Xiyuan, et al.
Published: (2026)

Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression
by: Wang, Ding, et al.
Published: (2024)

Predicting soccer matches with complex networks and machine learning
by: Baratela, Eduardo Alves, et al.
Published: (2024)

A machine learning approach to predict university enrolment choices through students' high school background in Italy
by: Priulla, Andrea, et al.
Published: (2024)

Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation
by: Sambadi Majumder, et al.
Published: (2025)

Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models
by: A., J. Moran, et al.
Published: (2023)

A novel nonconvex, smooth-at-origin penalty for statistical learning
by: John, Majnu, et al.
Published: (2022)

Is your data alignable? Principled and interpretable alignability testing and integration of single-cell data
by: Ma, Rong, et al.
Published: (2023)

Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies
by: Panat, Tanay, et al.
Published: (2025)

An unsupervised learning approach to evaluate questionnaire data -- what one can learn from violations of measurement invariance
by: Hahn-Klimroth, Max, et al.
Published: (2023)

The added value for MRI radiomics and deep-learning for glioblastoma prognostication compared to clinical and molecular information
by: Abler, D., et al.
Published: (2025)

Meta-models for transfer learning in source localisation
by: Bull, Lawrence A., et al.
Published: (2023)