FastTreeSHAP summary_plot plots interaction value instead of impact on model output
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 48/100
- Issue type
- Bug
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- data-visualization, machine-learning
Research direction
Start with fasttreeshap.summary_plot() using the reproduced 3-D shap_values shape and the bar plot example in the issue. Compare the generated feature ranking with the documented expected output, then verify that the completed behavior represents impact on model output rather than interaction values.
Written by the indexing model from the issue text.
Description
I am using FastTreeSHAP to help explained my random forest model prediction having over 40K samples and 24 features.
explainer = fasttreeshap.TreeExplainer(model, algorithm='auto' ,n_jobs=-1)
shap_values = explainer(X_test).values
shap_values.shape
(40682, 24, 5)
# plotting
fasttreeshap.summary_plot(shap_values, X_test, plot_type = 'bar')
The output figure is (showing top-5 features interaction):
I expected output figure to look similar to below image (FastTreeSHAP docs).
Is there any change in the summary_plot() method?
- Dominant language
- Python
- Stars
- 566
- Forks
- 38
- PR merge metrics
- No merged PRs in 30d
Contributor guide
First steps
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- Open a pull request that references the issue number.
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