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Automated and Interpretable Machine Learning - Microsoft Azure - Medium
Automated machine learning is based on a breakthrough from Microsoft’s Research Division. The approach combines ideas from collaborative filtering and Bayesian optimization to search an enormous…
automated  machine-learning  interpretability 
7 hours ago by hschilling
Interpretable Machine Learning
Machine learning algorithms usually operate as black boxes and it is unclear how they derived a certain decision. This book is a guide for practitioners to make machine learning decisions interpretable.
book  machine-learning  machinelearning  interpretability 
5 days ago by duracell999
[1811.10154] Please Stop Explaining Black Box Models for High Stakes Decisions
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward - it is to design models that are inherently interpretable.
black-box  machine-learning  cynthia-rudin  interpretability  via:vielmetti 
12 weeks ago by arsyed
[1101.0891] To Explain or to Predict?
Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal explanation and the assumption that models with high explanatory power are inherently of high predictive power. Conflation between explanation and prediction is common, yet the distinction must be understood for progressing scientific knowledge. While this distinction has been recognized in the philosophy of science, the statistical literature lacks a thorough discussion of the many differences that arise in the process of modeling for an explanatory versus a predictive goal. The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.
modeling  modeling-is-not-mathematics  statistics  prediction  interpretability  interestingness  (they-forgot-that-one)  philosophy-of-science  multiobjective-optimization  to-write-about 
march 2019 by Vaguery

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