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**Background**
Currently our ensembler components are provided as features only the predictions or predicted probabilities from the input pipelines.
**Proposal**
Alongside the input pipeline predicti…
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Hey there,
I fit a model with the following code:
```
# Define Super Learner
stack = make_learner(
Stack,
lrnr_glm,
lrnr_randomForest,
lrnr_xgboost,
lrnr_xgboost_limited…
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Add support for GAM as an option for metalearner_algorithm:
Currently supported:
"AUTO" (GLM with non negative weights & standardization turned off, and if validation_frame is present, then lambda…
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Deprecate `metaleaner_xyz` params in favour of `xyz`.
So it would change from
`H2OStackedEnsembleEstimator(metalearner_fold_column="fcol", weights_column="wcol")` to `H2OStackedEnsembleEstimator(fo…
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Hi, thank you for your nice code.
I tried to run your code but here's an issue
python train.py /path/to/data --dataset omniglot --num-ways 5 --num-shots 1 --use-cuda --step-size 0.4 …
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`## ----tmle3-ex2----------------------------------------------------------------
ist_data
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There are some datasets which have a skewed response, and if we don't log it before running AutoML (in particular, Stacked Ensemble GLM metalearner), we get bad results.
For regression problems, …
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``
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Hi,
I am wondering what are the effects of having `first-order=False`, and when should we use it?
From what I understand of the current implementation, `first_order` only affects the sample meth…
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AutoML is currently using default AUTO SE config for all its SE models.
We need to investigate if this is always the best choice, for example if a different metalearner algo could be picked in some s…