Results 41 to 50 of about 44,463 (307)
, 2023 Random forests (RF) best-performing model. Left, Posterior Pole (PPole) grid sample. Middle, grid of the local SHAP values. Right, waterfall plot for assessing feature contribution towards or against multiple sclerosis (MS).
Monica Hernandez (7584980) +5 more
core +2 more sources
SHAP values from the RF classifier.
, 2022 SHAP values were calculated from the random forest classifier trained to predict an artificial phenotype simulated from real metagenomes (n = 2147, p = 520 taxa; see Fig 2).
Nicholas D. Youngblut (4732542) +3 more
core +1 more source
Fairness by Explicability and Adversarial SHAP Learning [PDF]
, 2021 17 pages, 2 ...
James M. Hickey +2 more
openaire +2 more sources
Igneous and metamorphic processes in the Shap Granite and its aureole [PDF]
, 1986 The Shap Granite outcrops in eastern Cumbria, N.W. England and is a post—orogenic granite intruded during the Lower Devonian (ie 394 Ma) into rocks of Ordovician to Siturian age.
Caunt, Stephen Lloyd
core
Advanced Materials, EarlyView.Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
, 2023 The two representative SHAP force plots of a (A) dead and (B) survival patient. SHAP force plots are effective in interpreting the prediction value of the model in critical instances.
Dejin Le (14510200) +4 more
core +1 more source
Advanced Materials, EarlyView.This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
Advanced Materials, EarlyView.We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano +5 more
wiley +1 more source
On the Suitability of SHAP Explanations for Refining Classifications
Proceedings of the 14th International Conference on Agents and Artificial Intelligence, 2022 In industrial contexts, when an ML model classifies a sample as positive, it raises an alarm, which is subsequently sent to human analysts for verification. Reducing the number of false alarms upstream in an ML pipeline is paramount to reduce the workload of experts while increasing customers’ trust.
Yusuf Arslan +8 more
openaire +2 more sources