Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C Rudin - Nature machine intelligence, 2019 - nature.com
Black box machine learning models are currently being used for high-stakes decision
making throughout society, causing problems in healthcare, criminal justice and other …
making throughout society, causing problems in healthcare, criminal justice and other …
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Objectives The objective of this study was to compare performance of logistic regression
(LR) with machine learning (ML) for clinical prediction modeling in the literature. Study …
(LR) with machine learning (ML) for clinical prediction modeling in the literature. Study …
Anomaly detection in time series: a comprehensive evaluation
S Schmidl, P Wenig, T Papenbrock - Proceedings of the VLDB …, 2022 - dl.acm.org
Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …
ranging from manufacturing processes over finance applications to health care monitoring …
Interpretable machine learning–a brief history, state-of-the-art and challenges
We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
Wilds: A benchmark of in-the-wild distribution shifts
Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …
[BOOK][B] Fairness and machine learning: Limitations and opportunities
An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …
fairness and machine learning. Fairness and Machine Learning introduces advanced …
The promise of machine learning in predicting treatment outcomes in psychiatry
For many years, psychiatrists have tried to understand factors involved in response to
medications or psychotherapies, in order to personalize their treatment choices. There is …
medications or psychotherapies, in order to personalize their treatment choices. There is …
Ten quick tips for machine learning in computational biology
D Chicco - BioData mining, 2017 - Springer
Abstract Machine learning has become a pivotal tool for many projects in computational
biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical …
biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical …
A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and …
Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …
successful techniques in machine learning. Recently, the number of ensemble-based …
Text as data: The promise and pitfalls of automatic content analysis methods for political texts
J Grimmer, BM Stewart - Political analysis, 2013 - cambridge.org
Politics and political conflict often occur in the written and spoken word. Scholars have long
recognized this, but the massive costs of analyzing even moderately sized collections of …
recognized this, but the massive costs of analyzing even moderately sized collections of …