Comments (4)
Fair suggestion. The reason things that increase a prediction are on the left side pushing right is that otherwise things will go off the plot for large predictions (if we want to keep the baseline in view)
If there are useful and non-trivial alternatives I would be happy to include them to help make the SHAP values easier to communicate.
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Great. I'll have a think, and forward this on to some others interested. We could do some really awesome d3 visualizations, though that'd be more work. Will leave this open if someone else wants to pick it up for a PR.
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Is this visualizations done in this is done by D3.js in backend?
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Related Issues (20)
- BUG: Pytorch DeepExplainer SHAP explanations do not sum up to the model's output HOT 2
- BUG:
- BUG: NaN values created flattened Beeswarms HOT 1
- BUG: XGBRFRegressor invalid number of trees error with shap>=0.42 HOT 3
- BUG: TreeSHAP Interventional explanations segmentation fault HOT 3
- BUG: Problem of dimension during KernelExplainer HOT 6
- ENH: Not showing nan values on Beeswarm or violin plots
- BUG: Discrepancy among SHAP beeswarm and Seaborn swarmplots
- Intrepreting Siamese Network using SHAP values HOT 5
- ENH: Support SeLU activations for Tensorflow DeepExplainer
- BUG: AssertionError: The SHAP explanations do not sum up to the model's output HOT 1
- [Meta-issue] deprecation tracker for upcoming releases HOT 2
- BUG: missing **dmatrix_props in shape_interaction module HOT 1
- BUG: TreeExplainer ignores "link" argument passed by the Explainer API
- BUG: SHAP Partition explainer fails for a single token text input HOT 5
- BUG: Shap value cannot be calculated according to NLP tutorial on the original website HOT 17
- Segmentation Fault on MacOS with pytorch > 2.2.0 HOT 4
- ENH: Faster import performance HOT 1
- ENH: Label dots in scatterplot according to classes and add a legend
- BUG: SHAP DeepExplainer cannot get SHAP values from TorchScript model HOT 1
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