Comments (6)
PS @StatMixedML, I notice that you are interested in probabilistic forecasting, yet the estimators provided are, strictly speaking, probabilistic tabular regressors. That's not a big problem, as skpro
is integrated with the most common reduction compositors in sktime
, so any skpro
regressor can be directly used to create probabilistic forecasters via make_reduction
etc.
What would be nice, with your expertise, is some thinking around uncertainty estimates in recursive regression, that seems non-obvious. Let us know if you are interested in some methology research around that - or if you already have some solutions 😄
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@fkiraly Thanks for suggesting the skpro integration. Integrating the LSS-models into both skpro and sktime would be a fantastic extension! For now I suggest we focus on the XGBoostLSS/LightGBMLSS integrations, since the other two LSS frameworks are currently not maintained.
PS @StatMixedML, I notice that you are interested in probabilistic forecasting, yet the estimators provided are, strictly speaking, probabilistic tabular regressors.
That is correct. General purpose tree models are, without using parametric models in the leaf-nodes, not designed for forecasting, since they lack the ability to extrapolate beyond the training data. However, using the linear_tree
option in LightGBMLSS, gets us around this problem.
What would be nice, with your expertise, is some thinking around uncertainty estimates in recursive regression, that seems non-obvious. Let us know if you are interested in some methology research around that - or if you already have some solutions.
That sounds like an interesting problem. Can you maybe sketch the problem in more detail. We can also have the discussion via email if you want.
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That sounds like an interesting problem. Can you maybe sketch the problem in more detail.
Sure! Done in this discussion thread:
#185
Let me know if it makes sense, or if not, or if you would simply like more explanation.
We can also have the discussion via email if you want.
I know how academics are, so thanks for being considerate in this respect.
I think though, it's hard to argue precedence with public GitHub history. Of course, this could be disregarded or not mentioned (I have seen that a couple times), but the same can happen to any paper.
Hence I do not mind the discussion in public, even if novel methodological content comes out of it.
from skpro.
Integrating the LSS-models into both skpro and sktime would be a fantastic extension! For now I suggest we focus on the
XGBoostLSS
/LightGBMLSS
integrations, since the other two LSS frameworks are currently not maintained.
Thanks for your support! Let's get to it then 😃, contributions appreciated.
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I have created respective branches in the repos
- XGBoostLSS: https://github.com/StatMixedML/XGBoostLSS/tree/skpro_integration
- LightGBMLSS: https://github.com/StatMixedML/LightGBMLSS/tree/skpro_integration
Please work towards them before we actually merge it to master.
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hm, @StatMixedML, are you planning to write the estimator directly in the respective package?
Sure, that works - if you want to test it there, you can use check_estimator
from skpro.utils
.
Though for that set-up, you may like to consider relaxing your depedency bounds? See discussion in StatMixedML/XGBoostLSS#56
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Related Issues (20)
- [ENH] feature importance interface and tag for regression models
- [ENH] general Johnson QPD regression wrapper to reduce proba to quantile predictions
- [ENH] proba regression: reduction to multiclass classification HOT 7
- [BUG] test_methods_p not handling getattr after shifting
- [ENH] Multiple link function support for GLMs HOT 3
- [BUG] `sklearn.utils._param_validation.InvalidParameterError` thrown when using max_iter parameter in various sklearn regressors HOT 1
- [BUG] `Singular Matrix` error when testing param set with Cross Validation estimators
- [ENH] Design of a Bayesian model interface for sktime and skpro
- [ENH] outlier detection based on probabilitsic regressors
- [MNT] `numpy 2` compatibility HOT 3
- [ENH] merge `test_probabilistic_metrics` into `TestAllDistrMetrics` HOT 1
- [ENH] Improve efficiency of `Histogram Distribution` HOT 1
- [MNT] address incompatibility of `lifelines` with `scipy 1.14` HOT 1
- [BUG] `Histogram` distribution fails to construct on `numpy 2` HOT 2
- [BUG] `plot` method of the `Binomial` distribution shows incorrect pmf.
- Intervals/quantiles can be negative for models that can only make non-negative predictions HOT 3
- [ENH] interface `TweedieRegressor` from `sklearn` as `skpro` regressor HOT 6
- [ENH] interface GLM models from `glum`
- [ENH] Tweedie distribution, incl mathematics and design HOT 4
- [ENH] dummy supervised regressor HOT 5
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