Results 221 to 230 of about 29,456 (261)
Some of the next articles are maybe not open access.
Interventional SHAP Values and Interaction Values for Piecewise Linear Regression Trees
Proceedings of the AAAI Conference on Artificial Intelligence, 2023In recent years, game-theoretic Shapley values have gained increasing attention with respect to local model explanation by feature attributions. While the approach using Shapley values is model-independent, their (exact) computation is usually intractable, so efficient model-specific algorithms have been devised including approaches for decision trees ...
Artjom Zern +2 more
openaire +1 more source
Diagnosis of Parkinson's disease based on SHAP value feature selection
Biocybernetics and Biomedical Engineering, 2022Zhihui Liu, Xue Luo
exaly +2 more sources
Forecasting gold price with the XGBoost algorithm and SHAP interaction values
Annals of Operations Research, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ben Jabeur, Sami +2 more
openaire +3 more sources
Model Explainability using SHAP Values for LightGBM Predictions
2021 IEEE XVIIth International Conference on the Perspective Technologies and Methods in MEMS Design (MEMSTECH), 2021The paper aims at demonstrating the cutting-edge tool for machine learning models explainability leveraging LightGBM modelling. The proposed methodology improves inference performance, training time and significantly reduces the "fitting-to-noise" problem for complex datasets, and additionally, grants more insight into model predictions.
Michal Bugaj +2 more
openaire +1 more source
A SHAP Value-Based Approach to Stock Price Evaluation of Manufacturing Companies
2021 4th International Conference on Artificial Intelligence for Industries (AI4I), 2021In this study, we propose the usage of Shapley values as explanatory variables’ values to represent operational competence measures and try to relate such operational competencies to stock price evaluation. For this analysis, we used the data of Japanese manufacturing companies of three industries, automobiles, electronics and precision machinery ...
Yukari Shirota +4 more
openaire +2 more sources
Are SHAP Values Biased Towards High-Entropy Features?
2023n this paper, we examine the bias towards high-entropy features exhibited by SHAP values on tree-based structures such as classification and regression trees, random forests or gradient boosted trees. Previous work has shown that many feature importance measures for tree-based models assign higher values to high-entropy features, i.e.
Raphael Baudeu +2 more
openaire +3 more sources
Vector SHAP Values for Machine Learning Time Series Forecasting
Journal of ForecastingABSTRACTWe propose a new vector SHapley Additive exPlanations (SHAP) to interpret machine learning models for forecasting time series using lags of predictor variables. Unlike the standard SHAP measuring the contribution of each lag of each predictor variable, the proposed vector SHAP measures the contribution of the vector of the lags of each variable.
Dongwan Shin
exaly +2 more sources
LightGBM Scorecard Based on SHAP Values
SSRN Electronic Journal, 2023Yutae Choi, Eunji Cha
openaire +1 more source
Approximation of SHAP Values for Randomized Tree Ensembles
2022Markus Loecher, Dingyi Lai, Wu Qi
openaire +2 more sources
Explainability of Image Semantic Segmentation Through SHAP Values
2023Pierre Dardouillet +5 more
openaire +1 more source