Pymc-learn: Practical Probabilistic Machine Learning in Python

October 31, 2018 Β· Entered Twilight Β· πŸ› arXiv.org

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Repo contents: .gitignore, .pylintrc, .travis.yml, AUTHORS.txt, CHANGELOG.md, CODE_OF_CONDUCT.md, CONTRIBUTING.rst, LICENSE, MANIFEST.in, README.rst, docs, pmlearn, requirements-dev.txt, requirements.txt, scripts, setup.cfg, setup.py

Authors Daniel Emaasit arXiv ID 1811.00542 Category stat.ML: Machine Learning (Stat) Cross-listed cs.LG Citations 4 Venue arXiv.org Repository https://github.com/pymc-learn/pymc-learn ⭐ 232 Last Checked 2 months ago
Abstract
$\textit{Pymc-learn}$ is a Python package providing a variety of state-of-the-art probabilistic models for supervised and unsupervised machine learning. It is inspired by $\textit{scikit-learn}$ and focuses on bringing probabilistic machine learning to non-specialists. It uses a general-purpose high-level language that mimics $\textit{scikit-learn}$. Emphasis is put on ease of use, productivity, flexibility, performance, documentation, and an API consistent with $\textit{scikit-learn}$. It depends on $\textit{scikit-learn}$ and $\textit{pymc3}$ and is distributed under the new BSD-3 license, encouraging its use in both academia and industry. Source code, binaries, and documentation are available on http://github.com/pymc-learn/pymc-learn.
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