Pymc-learn: Practical Probabilistic Machine Learning in Python

TL;DR

Pymc-learn is a user-friendly Python package that integrates probabilistic models with scikit-learn-like API, making probabilistic machine learning accessible to non-specialists in both academic and industrial settings.

Contribution

It introduces a scikit-learn compatible interface for probabilistic models, simplifying their use and promoting broader adoption in machine learning workflows.

Findings

Provides a variety of probabilistic models for supervised and unsupervised learning

Offers an API consistent with scikit-learn for ease of use

Encourages adoption of probabilistic methods in diverse applications

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.