Results 61 to 70 of about 66,762 (195)
Interpretable Decisions Trees via Human-in-the-Loop-Learning
, 2022 Interactive machine learning (IML) enables models that incorporate human expertise because the human collaborates in the building of the learned model.
Hexel, Rene +2 more
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Scientific Reports, 2023 Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict.
Andrew Tritt +9 more
doaj +1 more source
Illuminating the Black Box: An Interpretable Machine Learning Based on Ensemble Trees
Deep learning has achieved significant success in the analysis of unstructured data, but its inherent black-box nature has led to numerous limitations in security-sensitive domains.
Lee, Yue-Shi;Yen, Show-Jane;Jiang, Wendong;Chen, Jiyuan;Chang, Chih-Yung
core +1 more source
INTERPRETABLE MACHINE LEARNING
In recent years, machine learning has rapidly evolved from a theoretical research domain into a core driver of innovation across industries, shaping decision-making processes in finance, healthcare, criminal justice, and beyond. As these intelligent systems increasingly affect real lives and critical outcomes, the demand for transparency and ...
Mrs. M. VIJAYA +5 more
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Scientific Reports, 2023 Recent studies revealed that gut microbiota modulates the response to cancer immunotherapy and fecal microbiota transplantation has clinical benefits in melanoma patients during treatment.
Min Oh, Liqing Zhang
doaj +1 more source
Interpretable Machine Learning algorithms with applications to bioinformatics
, 2023 openI modelli d’Interpretable Machine Learning sono essenziali per aumentare l’adozione del Machine Learning all'interno della società. Uno di questi viene chiamato decision rules, e fornisce una serie di regole atte a descrivere il criterio decisionale
MODOLO, RICCARDO
core
A Comprehensive Guide to Interpretable AI-Powered Discoveries in Astronomy
UniverseThe exponential growth of astronomical data necessitates the adoption of artificial intelligence (AI) and machine learning for timely and efficient scientific discovery.
Maggie Lieu
doaj +1 more source
Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning
Machine learning models have proven to be powerful tools to discover links between microstructure and properties of materials, but the black box nature of the models limits the physical insights one might gain from them.
Laurson, Lasse +2 more
core +1 more source
Pseudo-class part prototype networks for interpretable breast cancer classification
Scientific ReportsInterpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key to ...
Mohammad Amin Choukali +4 more
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Dual Interpretation of Machine Learning Forecasts
Machine learning predictions are typically interpreted as the sum of contributions of predictors. Yet, each out-of-sample prediction can also be expressed as a linear combination of in-sample values of the predicted variable, with weights corresponding to pairwise proximity scores between current and past economic events.
Goulet Coulombe, Philippe +2 more
openaire +3 more sources