🧰 How to Use This Template

Click the green "Use this template" button at the top of the page, then choose "Create a new repository".

This will create your own copy of this project, which you can modify freely — no need to fork!

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Beta–Binomial Classification

1. Quick Start

1.1 Prerequisites

• Docker and Docker Compose installed (Docker files remain unchanged)
• VS Code with Dev Containers extension (optional)

1.2 Setup and Run

# Make start.sh executable (if needed)
chmod +x start.sh

# Build and run the container
./start.sh

Alternatively:

docker-compose up --build -d

1.3 Verify

docker-compose ps

Confirm the container is up and port 8888 is mapped.

1.4 Run Python Scripts

In the VS Code terminal (inside the container), run:

python scripts/generate_beta_binomial_data.py

You should see the expected output (for example, confirmation message).

1.5 Work with Jupyter Notebooks in VS Code

• Open notebooks/1_Intro_Beta_Binomial.ipynb in VS Code.

• In VS Code, install the following extensions: Docker, Dev Containers, Python, Jupyter.

• VS Code will prompt you to select a kernel. Choose the Python kernel that is specified in the Dockerfile. The libraries that have been listed in the requirements.txt and installed in the docker will be run automatically.

• Now you can run and edit cells directly in VS Code.

1.6 Stop and Remove the Container

docker-compose down

2. Repository Structure

.
├── docs/                          # Educational docs
│   ├── Beta-Binomial-Classification.md
│   └── README.md
├── data/                          # Example and synthetic grouped counts
│   ├── beta_binomial_examples.csv
│   ├── beta_binomial_synthetic.csv
│   └── README.md
├── figures/                       # Generated visuals (mastery map)
│   └── README.md
├── notebooks/                     # Hands-on learning notebooks
│   ├── 1_Intro_Beta_Binomial.ipynb
│   ├── 2_Beta_Binomial_Classifier.ipynb
│   ├── 3_Visualization_Master_Map.ipynb
│   └── README.md
├── scripts/                       # Minimal, readable scripts
│   ├── generate_beta_binomial_data.py
│   ├── beta_binomial_classifier.py
│   ├── plot_mastery_map.py
│   └── README.md
├── requirements.txt
├── Dockerfile                     # untouched
├── docker-compose.yml             # untouched
└── start.sh                       # untouched

3. Learning Path

1. Read docs/Beta-Binomial-Classification.md for the concepts
2. Open notebooks/1_Intro_Beta_Binomial.ipynb to see posterior updates
3. Run notebooks/2_Beta_Binomial_Classifier.ipynb to classify data
4. Visualize mastery with notebooks/3_Visualization_Master_Map.ipynb

4. What You’ll Learn

• Posterior updates for Beta–Binomial (conjugacy)
• How to set priors and interpret uncertainty
• Mapping to mastery labels via credible bounds
• Visualizing attempts×successes mastery regions

5. Notes

• Media files in docs/ are placeholders and will be replaced later
• Docker setup is preserved as in the template