Explainable machine learning (XML), a subfield of AI, is focused on making complex AI models understandable to humans. This book highlights and explains the details of machine learning models used in geospatial data analysis. It demonstrates the need for a data-centric, explainable machine learning...
〈https://www.deeplearningbook.org/〉 Google Scholar [24] Z.C. Lipton The mythos of model interpretability Queue, 16 (3) (2016), pp. 30-57, 10.1145/3236386.3241340 Google Scholar [25] Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. ...
As Machine Learning and AI are becoming more and more popular an increasing number of organizations is adopting this new technology. Predictive modeling is helping processes becoming more efficient but also allow users to gain benefits. One can predict how much you are likely going to be earning ...
之后的章节里关注于一般的模型无关的方法来解释黑盒模型,比如特征重要性【例如:Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks,数据内部表示中的一部分表示相比其他表示更加transferrable,即这些表示在其他任务分布p(T)中都具有广泛的适用性,而非只在一个任务中有效。】、累积的局部效应,以及用Shap...
This study develops explainable machine learning (ML) models to predict the ultimate bending capacity of cold-formed steel (CFS) beams with staggered slotted perforations, focusing on distortional buckling behavior. Utilizing a dataset from 432 non-linear finite element analysis simulations of CFS Lipped...
Nature machine intelligence, 2(1), 2522–5839. Article Google Scholar Mantegna, R. N., & Stanley, H. E. (1999). Introduction to econophysics: Correlations and complexity in finance. Cambridge: Cambridge University Press. Book Google Scholar Molnar, C. (2019). Interpretable machine learning...
Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods
Book Google Scholar Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L (2018) Explaining explanations: an approach to evaluating interpretability of machine learning. https://arxiv.org/pdf/1806.00069.pdf Goldberg S, Shklovskiy-Kordi N, Zingerman B (2007) Time-oriented multi-image...
Overview of explainable machine learning framework for CVD risk prediction. Full size image Data description This study uses data from a nationally representative sample of adolescents who participated in the National Longitudinal Study of Adolescent to Adult Health (Add Health)29. The study followed ov...
Machine learning has become a common and powerful tool in materials research. As more data becomes available, with the use of high-performance computing and high-throughput experimentation, machine learning has proven potential to accelerate scientific research and techno...