Hi @mersoz-rccl -- I can see why you'd want to do this for linear regression. I could be wrong, but my guess is that it wouldn't have a measurable benefit for EBMs since they are much more flexible in what they can fit. I'd want to see a good example where it makes a difference before adding more complexity to the API given there's already a reasonable post-process solution of zeroing the intercept.

If you'd like to investigate this yourself, the place to modify the code that would implement it would be to add the following code:

update = booster.get_term_update()
update -= update[zeroith[term_idx]]
booster.set_term_update(term_idx, update)

In this location:

interpret/python/interpret-core/interpret/glassbox/_ebm/_boost.py

Line 155 in b17f9b5

The zeroith array needs to be passed into the boost function somehow. That needs to contain the per-term index of the bin that represents zero in the original data before binning. Probably the easiest way to do this, if you wanted to, would be to pass in the bin definitions via "feature_types" in the public API. If you pass in the bin definition [-1.1, 2.2, 3.3] then you can hard code the zero-index 2 for that term because the bins will be: "missing", [-inf, -1.1), [-1.1, 2.2), [2.2, 3.3), [3.3, +inf], "unknown". See: https://interpret.ml/docs/ebm-internals-regression.html

Disable Fit Intercept #577

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