Examples
========

This section contains comprehensive examples demonstrating how to use SACRO-ML for various machine learning scenarios and frameworks.

.. toctree::
   :maxdepth: 1

   notebook_examples
   scikit_learn_examples
   pytorch_examples
   risk_examples
   safemodel_examples
   unsupported_examples
   user_stories

Example summaries
-----------------

Below are short descriptions of each examples page. Click a link to open the corresponding examples document.

- :doc:`Notebook examples <notebook_examples>` — Interactive Jupyter notebooks demonstrating SACRO-ML usage across different models and workflows; good for quick-start tutorials and step-by-step demonstrations.
- :doc:`Scikit-learn examples <scikit_learn_examples>` — Examples focused on the scikit-learn ecosystem: dataset preparation, model training, evaluation, and disclosure risk analyses.
- :doc:`PyTorch examples <pytorch_examples>` — PyTorch-based examples (including CIFAR and simple model training) illustrating integration with SACRO-ML for deep learning use cases.
- :doc:`Risk examples <risk_examples>` — Scripts and notebooks centered on risk assessment methods, metrics, and reporting approaches.
- :doc:`SafeModel examples <safemodel_examples>` — Demonstrations of safe model construction and usage of the SACRO-ML SafeModel API and reporting utilities.
- :doc:`Unsupported examples <unsupported_examples>` — Experimental or unsupported example code showing edge-case usage and advanced techniques for experienced users.
- :doc:`User stories <user_stories>` — Real-world user story notebooks and scripts illustrating typical workflows and how SACRO-ML can be applied to practical scenarios.