On the other hand, complex machine learning models can provide key competitive differentiation. They are explainable — they work well and are highly dependable. And if the market segment is not highly regulated, organizations using them may use exploitative strategies to train large and complex AI ...
We applied an explainable machine learning (eXML) framework to quantify the global order of importance of hydro-climatic (predictor) variables on ETo, while highlighting the local dependencies and interactions amongst the predictors and ETo. The eXML framework also revealed the inflection points of ...
Introducing the Explainable Boosting Machine (EBM) EBM is an interpretable model developed at Microsoft Research*. It uses modern machine learning techniques like bagging, gradient boosting, and automatic interaction detection to breathe new life into traditional GAMs (Generalized Additive Models). This ...
Machine learning models of material properties accelerate materials discovery, reproducing density functional theory calculated results at a fraction of the cost1–6. To bridge the gap between theory and experiments, machine learning predictions need to
T. Interpretable and explainable machine learning for materials science and chemistry. Acc. Mater. Res. 3, 597–607 (2022). Article CAS Google Scholar Zhuo, Y., Mansouri Tehrani, A. & Brgoch, J. Predicting the band gaps of inorganic solids by machine learning. J. Phys. Chem. Lett. ...
Adj.1.interpretable- capable of being understood; "explainable phenomena" explainable explicable- capable of being explicated or accounted for; "explicable behavior" Based on WordNet 3.0, Farlex clipart collection. © 2003-2012 Princeton University, Farlex Inc. ...
Two feature sets were used in this research to evaluate the trade-off between simplicity and explainability vs performance which has been widely discussed in the machine learning literature34. Overall results show that the long model often demonstrates slightly superior performance to the short model....
Unintended consequences of machine learning in medicine. JAMA. 2017;318(6):517–8. Article PubMed Google Scholar Lundberg SM, Erion G, Chen H, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56–67. Article PubMed Google...
InterpretML: a unified framework for machine learning interpretability. Preprint at https://arxiv.org/abs/1909.09223 (2019). Feng, J., Lansford, J. L., Katsoulakis, M. A. & Vlachos, D. G. Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences. Sci....
Altogether, explainable machine-learning models can be deconvoluted to unveil new insights of how ICU patient features at the early stage interact with patient future events. Strengths of our study include its size, comprising 1,381 patients with AKI-D among 26,593 ICU admissions. We also have...