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It would be nice to have multi-quantile regression for approximating histogram in one go.
similar to
https://catboost.ai/en/docs/concepts/loss-functions-regression#MultiQuantile
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Since the XGBoost 1.6, we have been working on having multi-output support for the tree model. In 2.0, we will have the initial implementation for the vector-leaf-based multi-output model. This issue …
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Hi Catboost team,
first off, thanks for truly amazing library.
My question has to do with custom losses, I went through the following documentation file:
[https://github.com/catboost/catboos…
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- [ ] [Release Release 2.0.0 stable · dmlc/xgboost](https://github.com/dmlc/xgboost/releases/tag/v2.0.0)
# Release Release 2.0.0 stable · dmlc/xgboost
## Snippet
We are excited to announce the rele…
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Hi there,
I tried sklearn's `MLPRegressor` with the `RegressionModel` wrapper, and to my surprise, I was able to generate samples with `historical_forecasts` (e.g. with `num_samples = 1000`). How i…
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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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Currently, Autogluon Tabular predicts values without giving an estimate of the uncertainty.
Are you going to integrate methods like [conformal prediction](https://arxiv.org/pdf/2005.07972.pdf) or ano…
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It would be nice to have a clean implementation of the "official" EnbPI algorithms in `sktime` or `skpro`. While we do have conformal prediction algorithms, these are not the "originals".
Some of t…
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trying again for an overall design or design requirements for control chart
main original issue #4191 plus 2 PRs for earlier implementation
newer topics
- control charts based on beta distributio…
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This issue is to maintain all features request on one page.
Note to **contributors**: If you want to work for a requested feature, re-open the linked issue. Everyone is welcome to work on any of th…