sktime / skpro

:rocket: **Version 2.11.0 out now!** Read the release notes here.. is a library for supervised probabilistic prediction in python. It provides -like, compatible interfaces to: • tabular **supervised regressors for probabilistic prediction** - interval, quantile and distribution predictions • tabular **probabilistic time-to-event and survival prediction** - instance-individual survival distributions • **metrics to evaluate probabilistic predictions**, e.g., pinball loss, empirical coverage, CRPS, survival losses • **reductions** to turn regressors into probabilistic regressors, such as bootstrap or conformal • building **pipelines and composite models**, including tuning via probabilistic performance metrics • symbolic **probability distributions** with value domain of -s and -like interface | Overview | | |---|---| | **Open Source** | | | **Tutorials** | | | **Community** | | | **CI/CD** | | | **Code** | | | **Downloads** | ) | | **Citation** | | :books: Documentation | Documentation | | | -------------------------- | -------------------------------------------------------------- | | :star: **[Tutorials]** | New to skpro? Here's everything you need to know! | | :clipboard: **[Binder Notebooks]** | Example notebooks to play with in your browser. | | :woman_technologist: **[User Guides]** | How to use skpro and its features. | | :scissors: **[Extension Templates]** | How to build your own estimator using skpro's API. | | :control_knobs: **[API Reference]** | The detailed reference for skpro's API. | | :hammer_and_wrench: **[Changelog]** | Changes and version history. | | :deciduous_tree: **[Roadmap]** | skpro's software and community development plan. | | :pencil: **[Related Software]** | A list of related software. | [tutorials]: https://skpro.readthedocs.io/en/latest/tutorials.html [binder notebooks]: https://mybinder.org/v2/gh/sktime/skpro/main?filepath=examples [user guides]: https://skpro.readthedocs.io/en/latest/user_guide.html [extension templates]: https://github.com/sktime/skpro/tree/main/extension_templates [api reference]: https://skpro.readthedocs.io/en/latest/api_reference.html [changelog]: https://skpro.readthedocs.io/en/latest/changelog.html [roadmap]: https://skpro.readthedocs.io/en/latest/roadmap.html [related software]: https://skpro.readthedocs.io/en/latest/related_software.html :speech_balloon: Where to ask questions Questions and feedback are extremely welcome! We strongly believe in the value of sharing help publicly, as it allows a wider audience to benefit from it. is maintained by the community, we use the same social channels. | Type | Platforms | | ------------------------------- | --------------------------------------- | | :bug: **Bug Reports** | [GitHub Issue Tracker] | | :sparkles: **Feature Requests & Ideas** | [GitHub Issue Tracker] | | :woman_technologist: **Usage Questions** | [GitHub Discussions] · [Stack Overflow] | | :speech_balloon: **General Discussion** | [GitHub Discussions] | | :factory: **Contribution & Development** | channel · [Discord] | | :globe_with_meridians: **Community collaboration session** | [Discord] - Fridays 13 UTC, dev/meet-ups channel | [github issue tracker]: https://github.com/sktime/skpro/issues [github discussions]: https://github.com/sktime/skpro/discussions [stack overflow]: https://stackoverflow.com/questions/tagged/sktime [discord]: https://discord.com/invite/54ACzaFsn7 :dizzy: Features Our objective is to enhance the interoperability and usability of the AI model ecosystem: • skpro is compatible with [scikit-learn] and [sktime], e.g., an sktime proba forecaster can be built with an skpro proba regressor which in an sklearn regressor with proba mode added by skpro • skpro provides a mini-package management framework for first-party implementations, and for interfacing popular second- and third-party components, such as [cyclic-boosting], [MAPIE], or [ngboost] packages. [scikit-learn]: https://scikit-learn.org/stable/ [sktime]: https://www.sktime.net [MAPIE]: https://mapie.readthedocs.io/en/latest/ [cyclic-boosting]: https://cyclic-boosting.readthedocs.io/en/latest/ [ngboost]: https://stanfordmlgroup.github.io/projects/ngboost/ skpro curates libraries of components of the following types: | Module | Status | Links | |---|---|---| | **[Probabilistic tabular regression]** | maturing | Tutorial · API Reference · Extension Template | | **[Time-to-event (survival) prediction]** | maturing | Tutorial · API Reference · Extension Template | | **[Performance metrics]** | maturing | API Reference | | **[Probability distributions]** | maturing | Tutorial · API Reference · Extension Template | [Probabilistic tabular regression]: https://github.com/sktime/skpro/tree/main/skpro/regression [Time-to-event (survival) prediction]: https://github.com/sktime/skpro/tree/main/skpro/survival [Performance metrics]: https://github.com/sktime/skpro/tree/main/skpro/metrics [Probability distributions]: https://github.com/sktime/skpro/tree/main/skpro/distributions :hourglass_flowing_sand: Installing To install , use : or, with maximum dependencies, Releases are available as source packages and binary wheels. You can see all available wheels here. :zap: Quickstart Making probabilistic predictions Evaluating predictions :wave: How to get involved There are many ways to get involved with development of , which is developed by the community. We follow the all-contributors specification: all kinds of contributions are welcome - not just code. | Documentation | | | -------------------------- | -------------------------------------------------------------- | | :gift_heart: **[Contribute]** | How to contribute to skpro. | | :school_satchel: **[Mentoring]** | New to open source? Apply to our mentoring program! | | :date: **[Meetings]** | Join our discussions, tutorials, workshops, and sprints! | | :woman_mechanic: **[Developer Guides]** | How to further develop the skpro code base. | | :medal_sports: **[Contributors]** | A list of a…