Understand the project
Practical overview
When it fits
Use it when you want a focused, individually runnable algorithm example rather than a hosted model service or integrated development workflow.
Who it is for
- Learners exploring how individual machine-learning algorithms work.
- Developers seeking small Python examples as references when studying or prototyping algorithm behavior.
Documented uses
- Inspect and run a specific algorithm example.
- Compare alternative multi-armed bandit approaches.
- Find compact examples across neural networks, decision processes and dimensionality reduction.
Before you choose
Requirements & limits
The repository is public and its metadata lists a BSD-2-Clause license.
- Recipes are standalone examples rather than a documented unified package or hosted service.
- The README identifies example files but does not describe a general installation process or dependencies.
- The README suggests contributions should be reasonably small and include usage.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Source-based practical guide
Run an individual ML-Recipes example
Run a standalone Python recipe to see its usage and result.
Before you start
- A Python environment.
Steps
Choose a recipe from the repository, such as the epsilon-greedy example at recipes/MAB/greedy.py or the softmax example at recipes/MAB/softmax.py.
Run that specific recipe in your Python environment.
Expected outcome
The recipe demonstrates its usage and result.
Guide limitations
- The README does not specify Python versions, dependencies, or detailed run commands.
- Examples are standalone recipes; their individual behavior may differ.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- rougier/ML-Recipes · Official homepage title · retained in its source language