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* Currently the metalearner is hardcoded as a default H2O GLM with non-negative weights. (`non_negative = TRUE`)
* We need to add a `metalearner_algorithm` argument to allow customization of the meta…
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In h2oEnsemble, we have a `keep_levelone_data` arg to keep the level one data frame that's constructed for the metalearning step. Let's also add this argument (rename to `keep_levelone_frame`) to Sta…
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We don't support using offsets (offset_column) in Stacked Ensemble. You can use offsets in the base learners, but Stacked Ensemble metalearner is hardcoded and doesn't honor that. We need to check:
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Hi,
People start to cite YAHPO for LCBench. As one of the authors of LCBench, I'm of course not happy that you get the citations now. Please be very explicit that people should cite the original re…
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Permutation feature importance is a great way to get feature importance in a model-agnostic fashion. All our algorithms (except Stacked Ensemble at the moment) have built-in feature importance, but i…
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Currently, a "Stacked Ensemble" model object only contains the info/metadata that a normal "H2OModel" has. We should add the ability to access the name/ID of the metalearning model (at the very least…
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Interpretability is getting more and more critical for Machine Learning Modeling. Especially in the healthcare and financial industry. For example, it is not good if the model predicts the patient wil…
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This ticket is now just to add the {{metalearner_transform}} option, defaulting to {{”NONE”}} (same as before) but the non-deafult option is {{”Logit”}} which takes the logit transform of the CV preds…
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{noformat}Defining a Stacked Ensemble Model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Parameters are optional unless specified as *required*.
Algorithm-specific parameters
'''''''''''''''''''''''''''''
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using commit didn't help (I think?): here is where I ran:
```
#
git clone git@github.com:DistributedComponents/InfSeqExt.git deps/InfSeqExt
(cd deps/InfSeqExt && opam install -y .)
# above worked…