Comments (1)
The decision_function returns raw scores, so an easy way to do this is:
init_scores = EBM_offset.decision_function(X)
probs = EBM_main.predict_proba(X, init_scores)
Or, if you prefer, you can merge the two models. This is more complicated, but there's a related example in:
https://interpret.ml/docs/python/examples/custom-interactions.html
from interpret.
Related Issues (20)
- 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
- Merging two EBM regressors leads to model that has NaNs in attributes HOT 3
- Bug: Pandas DataFrames columns names not verified at prediction time HOT 6
- Add a new interpretable algorithm, Automatic Piecewise Linear Regression HOT 17
- Smoothness over variable regions with outlier outcome values HOT 11
- Create submodels of an ExplainableBoostingRegressor(outer_bags=14)? HOT 1
- Fitting ExplainableBoostingClassifier in new process causes long delay and zombie processes HOT 3
- Question: Adding offset variable during training HOT 1
- Relation between EBM scores and model parameters HOT 5
- Merge ebm with different subset of variables HOT 6
- Feature selection/preprocessing before EBM fitting to speed up fitting? HOT 1
- The output score of an individual-level feature HOT 4
- Visualize or get classes from the ClassificationTree explain_global() leaves through rules
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from interpret.