Comments (2)
Yes, calling SuperLearner.summary()
returns the weights given to each learner.
If this is related to #167 then things aren't as straightforward. summary()
is not called because of the volume of output. The cross-fit procedure means that there are multiple SL fit to each partition of the data (a min of 2 for single cross-fit). A number of different partitions are done by the cross-fitting procedure. As the recommendation is like 100 different partitions, the weights for each partition are not output (printed or saved).
I will consider how that could be added to things like SingleCrossfitTMLE
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Thank you @pzivich! able to know weights (at least average weights across each partition) for the model candidates would be very helpful!
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Related Issues (20)
- IPTW handle PerfectSeparationErrors in the marginal structural model better
- AIPW for survival analysis ? HOT 1
- Dual treatments
- ValueError better pytest strategy
- Package compatibility? HOT 2
- Update documentation (and possibly re-organize) HOT 2
- MonteCarloGFormula
- Add Odds Ratio and other estimands for AIPTW and TMLE
- Addition of meta-analysis tools
- add p-value column in a forrest plot/ effectmeasureplot HOT 2
- Enhancement in graphics.py to change odds text size HOT 1
- Saving DAGs programatically HOT 11
- sklearn dependancy in setup.py should be scikit-learn HOT 1
- AIPW formula equivalent to what's in the literature? HOT 2
- Perfect separation error for using `SingleCrossfitTMLE` HOT 2
- SingleCrossFit `invalid value encountered in log` HOT 8
- Unable to install latest 0.9.0 version through pip HOT 7
- Risk Ratio Summary HOT 1
- The default regression argument of zepid.base.interaction_contrast_ratio differs from the description in the documentation. HOT 4
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