Pages that link to "Item:Q2238680"
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The following pages link to Explaining individual predictions when features are dependent: more accurate approximations to Shapley values (Q2238680):
Displaying 24 items.
- Interpretable concept-based classification with Shapley values (Q2038054) (← links)
- Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance (Q2066736) (← links)
- Assessment of the influence of features on a classification problem: an application to COVID-19 patients (Q2077933) (← links)
- PredDiff: explanations and interactions from conditional expectations (Q2093377) (← links)
- Wasserstein-based fairness interpretability framework for machine learning models (Q2102385) (← links)
- Explanation with the winter value: efficient computation for hierarchical Choquet integrals (Q2105574) (← links)
- On Shapley value interpretability in concept-based learning with formal concept analysis (Q2107486) (← links)
- Relation between prognostics predictor evaluation metrics and local interpretability SHAP values (Q2124455) (← links)
- ESG score prediction through random forest algorithm (Q2155224) (← links)
- Grouped feature importance and combined features effect plot (Q2172623) (← links)
- Explaining predictive models using Shapley values and non-parametric vine copulas (Q2236381) (← links)
- \( \mathcal{G} \)-LIME: statistical learning for local interpretations of deep neural networks using global priors (Q2680795) (← links)
- An efficient explanation of individual classifications using game theory (Q2896017) (← links)
- (Q5053199) (← links)
- On the Tractability of SHAP Explanations (Q5094036) (← links)
- Local interpretation of supervised learning models based on high dimensional model representation (Q6057385) (← links)
- A \(k\)-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning (Q6067036) (← links)
- Explainable subgradient tree boosting for prescriptive analytics in operations management (Q6087515) (← links)
- Considerations when learning additive explanations for black-box models (Q6176233) (← links)
- Interpreting machine-learning models in transformed feature space with an application to remote-sensing classification (Q6176236) (← links)
- A comparative study of methods for estimating model-agnostic Shapley value explanations (Q6609084) (← links)
- On marginal feature attributions of tree-based models (Q6620135) (← links)
- An illustration of model agnostic explainability methods applied to environmental data (Q6626539) (← links)
- Explainable machine learning for financial risk management: two practical use cases (Q6633384) (← links)
