Setting the regularization parameter optimally
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
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- Issue type
- Bug
- Clarity
- Needs clarification
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- Stale
- Tech stack
- python, scikit-learn
- Domain
- machine-learning
Research direction
Start with the SparseLinearDMLCateEstimator configuration shown in the issue, using LassoCV and MultiTaskLassoCV with custom folds and max_iter=100000. Reproduce the alpha convergence warning, then compare the behavior with automatic versus custom cross-validation. Done means the warning has a reproducible diagnosis and the expected configuration or resolution is clearly identified.
Written by the indexing model from the issue text.
Description
Hi and thank you for the improvements!
I am facing the following error, though setting the number of splits optimally according to issue #94. In particular, the warning appearing about alpha is:
Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems. As a consequence of the error, I tried to increase the maximum number of iterations as follows:
from econml.dml import SparseLinearDMLCateEstimator
est = SparseLinearDMLCateEstimator(model_y = LassoCV(cv=[(fold00, fold11), (fold11, fold00)]),
model_t = MultiTaskLassoCV(cv=[(fold00, fold11), (fold11, fold00)]),
n_splits = [(fold0, fold1), (fold1, fold0)],
linear_first_stages=False,
featurizer=PolynomialFeatures(degree=4, include_bias=False),
max_iter=100000)
But still the issue remains. Maybe the cross-validation in first and final stages should be left automatic?
Thank you,
Federico
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