StatMixedML / LightGBMLSS
An extension of LightGBM to probabilistic modelling
Why this job: works in the areas we cover, but does not say which job it does
What it does
We introduce a comprehensive framework that models and predicts the full conditional distribution of a univariate target as a function of covariates. Choosing from a wide range of continuous, discrete, and mixed discrete-continuous distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of LightGBM, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 41 days ago
- Stars
- 396
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v0.6.1
- Released 11 months ago
- Built with
- Python
- 100% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 77% closed
- 7 still open
- Pull requests
- 7 merged
- 7 waiting
- Releases
- 8
- 3 in the last year
- Contributing guide (opens in new tab)
- No security policy
- Code of conduct (opens in new tab)
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More: languages, topics and project record
Languages
- Python 100%
Topics
- machine-learning
- lightgbm
- gamlss
- uncertainty-estimation
- prediction-intervals
- distributional-regression
- probabilistic-forecasting
- normalizing-flows
- mixture-density-model
Project record
- JobOther AI tools
- StatusListed
- Commits233
- Forks39
- Watching11
- Stars in 28 days0
- CreatedJul 3, 2020
- First seen by usNov 7, 2026
- CheckedNov 8, 2026