Results 31 to 40 of about 24,298 (249)

Shapley Additive Explanations for Knowledge Discovery via Surrogate Models

AIAA SCITECH 2023 Forum, 2023
It is sometimes desirable to delve further into how the inputs affect the output in design optimization and uncertainty analysis. Surrogate models such as Gaussian Process Regression and support vector regression are useful for such tasks and can be further enhanced by introducing advanced post-processing methods.
Palar, Pramudita Satria, Zuhal, Lavi Rizki, Dwianto, Yohanes Bimo, Shimoyama, Koji, Morlier, Joseph   +4 more
openaire   +2 more sources

Bankruptcy prediction using machine learning and Shapley additive explanations

Review of Quantitative Finance and Accounting, 2023
Recently, ensemble-based machine learning models have been widely used and have demonstrated their efficiency in bankruptcy prediction. However, these algorithms are black box models and people cannot understand why they make their forecasts. This explains why interpretability methods in machine learning attract attention from many artificial ...
Nguyen, Hoang Hiep, Viviani, Jean-Laurent, Ben Jabeur, Sami   +2 more
openaire   +3 more sources

SHapley Additive exPlanations (SHAP) for Landslide Susceptibility Models: Shedding Light on Explainable AI [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
This research examines the effectiveness of the SHapley Additive exPlanations (SHAP) approach in enhancing the interpretability of landslide susceptibility models. With the growing popularity of machine learning, we aim to understand how geoenvironmental
H. Al-Najjar, B. Kalantar, B. Pradhan, G. Beydoun, N. Ueda   +4 more
doaj   +1 more source

Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations [PDF]

CoRR, 2022
Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This letter first seeks to showcase the benefits of SHapley Additive exPlanations (SHAP) for understanding the outcomes of ML models, which are increasingly being used.
Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad, Samuel Chevalier, Rahul Nellikkath, Ilgiz Murzakhanov, Spyros Chatzivasileiadis, Panagiotis N. Papadopoulos   +7 more
openaire   +4 more sources

Visualization of explainable artificial intelligence for GeoAI

Frontiers in Computer Science
Shapley additive explanations are a widely used technique for explaining machine learning models. They can be applied to basically any type of model and provide both global and local explanations.
Cédric Roussel
doaj   +1 more source

The Prediction of the Tibetan Plateau Thermal Condition with Machine Learning and Shapley Additive Explanation

Remote Sensing, 2022
The thermal condition over the Tibetan Plateau (TP) plays a vital role in the South Asian high (SAH) and the Asian summer monsoon (ASM); however, its prediction skill is still low. Here, two machine learning models are employed to address this problem.
Yuheng Tang, Anmin Duan, Chunyan Xiao, Yue Xin   +3 more
openaire   +2 more sources

Diabetes Risk Prediction using Shapley Additive Explanations for Feature Engineering

Journal of Informatics and Web Engineering
Diabetes is prevalent globally, expected to increase in the next few years. This includes people with different types of diabetes including type 1 diabetes and type 2 diabetes.
Chinwe Miracle Chituru, Sin-Ban Ho, Ian Chai   +2 more
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

CLE-SH: Comprehensive Literal Explanation Package for SHapley Values by Statistical Validity

IEEE Access
Recently, SHapley Additive exPlanations (SHAP) has been widely utilized in various research domains. This is particularly evident in application fields, where SHAP analysis serves as a crucial tool for identifying biomarkers and assisting in result ...
Kyungjin Kim, Youngro Lee, Jongmo Seo
doaj   +1 more source

Statistical Inference and Learning for Shapley Additive Explanations (SHAP)

CoRR
The SHAP (short for Shapley additive explanation) framework has become an essential tool for attributing importance to variables in predictive tasks. In model-agnostic settings, SHAP uses the concept of Shapley values from cooperative game theory to fairly allocate credit to the features in a vector $X$ based on their contribution to an outcome $Y ...
Justin Whitehouse, Ayush Sawarni, Vasilis Syrgkanis   +2 more
openaire   +2 more sources