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Dear Py-Boost developers,
Thanks for the very interesting paper and for making the code publicly available.
I am the author of [XGBoostLSS](https://github.com/StatMixedML/XGBoostLSS) and [Light…
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Classifiers have a `predict_proba` method that makes it possible to quantify probabilistic ally the certainty in the predictions for a given input `X_i`.
Currently most regressors in scikit-learn o…
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The PR https://github.com/sktime/skpro/pull/215 has added `ngboost` as an `skpro` regressor.
The package can also be invoked for survival prediction, i.e., for using censoring information `C` as in…
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is it possible to use ngboost as a regressor for forecaster?
if yes how should we define it?
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### DAG Description
It's necessary to create dags to weekly update the results of the dashboard:
https://epigraphhub.org/covidch/
### Basic Workflow
- what would be the expected result?
Th…
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If you consider the example here:
https://github.com/stanfordmlgroup/ngboost/blob/master/examples/model_interpretation/SHAP_plots.ipynb
But instead use NGBRegressor(Dist=LogNormal) where LogNorm…
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Update by @fkiraly - we should interface `ngboost` regressors as probabilistic supervised (tabular) regressors.
As discussed in the below, this should be an `skpro` regressor, which in turn can be u…
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I would like to be able to run a GEE analysis using an unstructured covariance. In R this is very easy to do, e.g:
```
library(geepack)
geeglm(
outcome ~ . -cluster_id,
id = cluster_id,
…
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Puzzling to see such methods - it is proven they don't work. Relying on normality assumptions and parametric methods can not produce good prediction intervals.
https://medium.com/@valeman/does-ngbo…
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Dear authors,
This is a very nice work! And I made it run on my laptop successfully.
There is a thing that I feel a little bit confused.
On example 14, besides the codes the authors provide, I…