# Pymc-learn: Practical Probabilistic Machine Learning in Python

arXiv: https://arxiv.org/abs/1811.00542 · HTML: https://mlsanity.com/paper/1811.00542/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [High dimensional Bayesian Optimization Algorithm for Complex System in Time Series](https://mlsanity.com/paper/2108.02289/) (2021)

## Abstract (arXiv)

textitPymc-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 textitscikit-learn and focuses on bringing probabilistic machine learning to non-specialists. It uses a general-purpose high-level language that mimics textitscikit-learn. Emphasis is put on ease of use, productivity, flexibility, performance, documentation, and an API consistent with textitscikit-learn. It depends on textitscikit-learn and textitpymc3 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.