Comments (4)
Hi @sauvikd --
One of the very nice things about EBMs, and GAMs in general, is that the scores are used when calculating the predictions AND what are shown on the graphs. EBMs have no hidden information.
For graphing though, you also need the "bins" attribute, which for continuous features are the locations on the X-axis where the bin transitions occur.
There's some code which creates graphs here: #325
And you might also find our documentation on EBM internals useful: https://interpret.ml/docs/ebm-internals-regression.html
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Thank you so much @paulbkoch for answering my query. It helped me solve my problem.
There's a 2nd part of my query if you can answer my query. Although I can retrieve the individual feature importance plots showing scores using the following piece of code.
for feature_idx, feature in enumerate(best_ebm_model.term_names_):
iplot(ebm_global_explainer.visualize(feature_idx))
How can I separately plot the - EBM Summary Global Importance bar plot, like the one below?
from interpret.
Hi @sauvikd --
We usually recommend the show function:
show(ebm_global_explainer)
But, if you want to use iplot, try this:
iplot(ebm_global_explainer.visualize(None))
from interpret.
Thanks @paulbkoch, it worked for me.
I was looking to save these images without manually taking a screenshot.
from interpret.
Related Issues (20)
- Query: performance prospects on massive data sets (curse of dimensionality?) HOT 3
- How to speed up EBM model? Unbelievable slow. HOT 9
- Question: Parallel boosting? HOT 4
- Integrate EBM into the pytorch framework HOT 7
- Visualising Decision Tree explainer gives a Cytoscape object which is not savable to my local machine HOT 2
- [DP-EBM] Question regarding range R and sensitivity
- Support for more parameters in the Differentially Private models HOT 1
- NAM Model HOT 1
- Some hyperparameter questions HOT 3
- Operations when merging EBM HOT 6
- EBM Classifier Global Feature Importance x Random Forest Classifier with Morris Sensitivity Analysis HOT 1
- possibility of adding `sample_weight` to `interpret.glassbox.ClassificationTree` HOT 6
- 2d PDP Z-axis colours appear too similar HOT 1
- Exporting EBM as PMML HOT 3
- Feature Request: Passing Validation Set or Index HOT 2
- Explore the data with continuous output and category input HOT 4
- Using the init_score in EBM Classifier HOT 1
- Merging two EBM regressors leads to model that has NaNs in attributes
- Bug: Pandas DataFrames columns names not verified at prediction time HOT 6
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