Papers

2025

Goode, K., Tucker, J. D., Ries, D., & Hofmann, H. (2025). An explainable pipeline for machine learning with functional data. Submitted to Annals of Applied Statistics.

Ries, D., Goode, K., McClernon, K., and Hillman, B.: Using feature importance as exploratory data analysis tool on earth system models, Geosci. Model Dev. Discuss. Preprint, https://gmd.copernicus.org/preprints/gmd-2024-133/, in review.

2024

Goode, K., D. Ries, and K. McClernon, Characterizing climate pathways using feature importance on echo state networks, Stat. Anal. Data Min.: ASA Data Sci. J. 17 (2024), e11706. https://doi.org/10.1002/sam.11706

Goode, K. J., & Tucker, J. D. (2024). FORESTR: Finding, Organizing, Representing, Explaining, Summarizing, and Thinning Random forests (No. SAND2024-13686R). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States). https://www.osti.gov/servlets/purl/2472741.

McClernon, K., Goode, K., & Ries, D. (2024). A comparison of model validation approaches for echo state networks using climate model replicates. Spatial Statistics, 100813. https://www.sciencedirect.com/science/article/pii/S2211675324000046.

McCombs, A. L., Stricklin, M. A., Goode, K., Huerta, J. G., Shuler, K., Tucker, J. D., Zhang, A., Sweet, L., & Ries, D. (2024). Inverse prediction of PuO2 processing conditions using bayesian seemingly unrelated regression with functional data. Frontiers in Nuclear Engineering, 3, 1331349. https://www.frontiersin.org/journals/nuclear-engineering/articles/10.3389/fnuen.2024.1331349/full.

2023

Goode, K., M.J. Weber, and P.M. Dixon. 2023. “WhoseEgg: classification software for invasive carp eggs”. PeerJ. 11:e14787. https://doi.org/10.7717/peerj.14787.

Goode, K., M.J. Weber, A. Matthews, and C.L. Pierce. 2023. “Evaluation of a Random Forest Model to Identify Invasive Carp Eggs Based on Morphometric Features”. North Am J Fish Manage. 43: 46-60. https://doi.org/10.1002/nafm.10616

2022

Ausdemore, M. A., A. McCombs, D. Ries, A. Zhang, K. Shuler, K. Goode, J. D. Tucker, and J. G. Huerta. 2022. A Probabilistic Inverse “Prediction Method for Predicting Plutonium Processing Conditions”. Frontiers in Nuclear Engineering-Nuclear Materials. https://doi.org/10.3389/fnuen.2022.1083164

2021

Goode, K. and H. Hofmann. “Visual diagnostics of an explainer model: Tools for the assessment of LIME explanations.” 2021. Stat Anal Data Min: The ASA Data Sci Journal 14:185–200. https://doi.org/10.1002/sam.11500. GitHub.

Adams, J., Goode, K., Michalenko, J., Lewis, P., Ries, D., and Zollweg, J. 2021. Semi-supervised Bayesian Low-shot Learning. https://doi.org/10.2172/1821543

2020

Goode, K., D. Ries, and J. Zollweg. “Explaining Neural Networks with Functional Data Using PCA and Feature Importance”. AAAI 2020 Fall Symposium on AI in the Government and Public Sector. November 13-14, 2020. https://arxiv.org/abs/2010.12063. YouTube Video.

Dixon, P.M., K. Goode, C. Lay. 2020. “Profile likelihood confidence intervals for ECx”. Iowa State Digital Library. Statistics Technical Reports. https://lib.dr.iastate.edu/stat_las_reports/1

Ball, E.E., K. Goode, and M.J. Weber. 2020. “Effects of Transport Duration and Water Quality on Age-0 Walleye Stress and Survival”. North American Journal of Aquaculture 82:33–42. https://doi.org/10.1002/naaq.10114.