Pymc-learn: Practical Probabilistic Machine Learning in Python
This work addresses the problem of accessibility to probabilistic machine learning for non-specialists in academia and industry, though it is incremental as it builds on existing tools like scikit-learn and pymc3.
The authors tackled the challenge of making probabilistic machine learning accessible to non-specialists by developing Pymc-learn, a Python package that provides state-of-the-art probabilistic models for supervised and unsupervised learning, resulting in a tool that emphasizes ease of use, productivity, and consistency with scikit-learn.
$\textit{Pymc-learn}$ 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.