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Hi,
Thank you for all the work you do. I have some concept questions if contributors would like to weigh in:
1. Is there intuition for why featurizer is not implemented for the ForestDRLearner (b…
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# Overview
The current implementation has some nice features for handling iterative data and provides early exit conditions. Unfortunately, these features are harder to maintain as we need to handl…
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Hi,
Thanks again for your continuing support on this amazing package.
Could you please help me with this error message? Can't we use "saem" in weightit()?
data("lalonde", package = "cobalt"…
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Specifically, I think separating the modules in this into subpackages (i.e. reexported as part of a larger overall BetaML package) would help a lot with discoverability; for instance, the problem I me…
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We should implement some sort of outlier detection and screening for the hourly values reported in CEMS. This outlier detection could use a combination of statistical methods and physics-based methods…
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I did a benchmark over the 2 imputation methods over following procedure
1. Run a small imputation benchmark on KNN and softImpute(SVD)
1. subset the first 10,000 of the CpG sites with totally …
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`Neto, Ary S.*, Rodrigo O. Deliberato*, Alistair E. W. Johnson*, Lieuwe D. Bos*, Pedro Amorim, Silvio Moreto Pereira, Denise Carnieli Cazati et al. "Mechanical power of ventilation is associated with …
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Recently I have read some articles about data imputation,and found that some methods are for multivariate data while others for multidimensional data,I wonder what is the difference between these.Coul…
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## Describe the issue linked to the documentation
### Context
We discussed with @glemaitre and @GaelVaroquaux about documenting missing-values practices for prediction in scikit-learn as part of…
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Allow users to fill in missing data in the input data arrays. Propose
- using the [data imputation methods in the statsmodels package](https://docs.w3cub.com/statsmodels/generated/statsmodels.imputa…