Kim, Been, Rajiv Khanna, and Oluwasanmi O. Koyejo. “Examples are not enough, learn to criticize! Criticism for interpretability.” Advances in Neural Information Processing Systems (2016). Doshi-Velez, Finale, and Been Kim. “Towards a rigorous science of interpretable machine learning,” no....
Safe and interpretable machine learning: a methodological review. In C. Moewes & A. Nurnberger (Eds.), Computational Intelligence in Intelligent Data Analysis . Dordrecht: Springer.Clemens Otte. Safe and interpretable machine learning: A methodological review. In Com- putational Intelligence in ...
Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Extracting meaningful phys...
Machine learning algorithms This study considered interpretability to be a core requirement for machine learning model selection25,26. Extreme gradient boosting (XGBoost) and logistic regression (LR) algorithms were used to predict whether a patient with mild COVID-19 symptoms would develop into a sev...
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Here I review a number of techniques for interpreting machine learning models at the level of the system, the variable and the individual prediction as well as methods for handling non锕妌dependent data. I also discuss the limits of interpretability for different methods and demonstrate these ...
ContributedbyBinYu,July1,2019(sentforreviewJanuary16,2019;reviewedbyRichCaruanaandGilesHooker)Machine-learningmodelshavedemonstratedgreatsuccessinlearningcomplexpatternsthatenablethemtomakepredic-tionsaboutunobserveddata.Inadditiontousingmodelsforprediction,theabilitytointerpretwhatamodelhaslearnedisreceivinganincreasing...
making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap ...
Izzy Newsham (Data curation [lead], Formal analysis [lead], Investigation [lead], Methodology [lead], Resources [equal], Software [lead], Validation [lead], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Marcin Sendera (Data curation [supporting],...
Opening the black box: interpretable machine learning for geneticists 1 2 Christina B. Azodi, Jiliang Tang, Shin-Han Shiu 3 4 1 Department of Plant Biology, Michigan State University, East Lansing, MI, USA 5 2 The DOE Great Lakes Bioenergy Research Center, Michigan State University, East Lan...