Definitions, methods, and applications in interpretable machine learning
Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …
that enable them to make predictions about unobserved data. In addition to using models for …
Interpretable machine learning: definitions, methods, and applications
Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …
that enable them to make predictions about unobserved data. In addition to using models for …
Veridical data science
B Yu - Proceedings of the 13th international conference on …, 2020 - dl.acm.org
Veridical data science extracts reliable and reproducible information from data, with an
enriched technical language to communicate and evaluate empirical evidence in the context …
enriched technical language to communicate and evaluate empirical evidence in the context …
Machine learning assisted discovery of interactions between pesticides, phthalates, phenols, and trace elements in child neurodevelopment
A growing body of literature suggests that developmental exposure to individual or mixtures
of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD) …
of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD) …
Prenatal lead exposure is associated with reduced abundance of beneficial gut microbial cliques in late childhood: an investigation using microbial co-occurrence …
Many analytical methods used in gut microbiome research focus on either single bacterial
taxa or the whole microbiome, ignoring multibacteria relationships (microbial cliques). We …
taxa or the whole microbiome, ignoring multibacteria relationships (microbial cliques). We …
Sirus: Stable and interpretable rule set for classification
State-of-the-art learning algorithms, such as random forests or neural networks, are often
qualified as “black-boxes” because of the high number and complexity of operations …
qualified as “black-boxes” because of the high number and complexity of operations …
Fault diagnosis of electrical equipment through thermal imaging and interpretable machine learning applied on a newly-introduced dataset
In this study, an interpretable, fully automated pipeline for condition monitoring of electrical
equipment using thermal imaging is proposed. A wider array of defects in comparison with …
equipment using thermal imaging is proposed. A wider array of defects in comparison with …
[HTML][HTML] Prenatal metal exposures and childhood gut microbial signatures are associated with depression score in late childhood
Background Childhood depression is a major public health issue worldwide. Previous
studies have linked both prenatal metal exposures and the gut microbiome to depression in …
studies have linked both prenatal metal exposures and the gut microbiome to depression in …
[PDF][PDF] Three principles of data science: predictability, computability, and stability (PCS)
B Yu - 2018 - escholarship.org
Data science is a field of evidence seeking that combines data with domain information to
generate new knowledge. The data science life cycle (DSLC) begins with a domain question …
generate new knowledge. The data science life cycle (DSLC) begins with a domain question …
A hydrologic functional approach for improving large‐sample hydrology performance in poorly gauged regions
Hydrologic functions of catchments are intrinsically diverse and defined as the ways
catchments partition, store, and drain rainfall and snowmelt. Large‐sample hydrology (LSH) …
catchments partition, store, and drain rainfall and snowmelt. Large‐sample hydrology (LSH) …