Tutorials

Interactive Jupyter notebooks demonstrating how to use MCGrad for multicalibration.

Available Tutorials

Tutorial	Description	Launch
MCGrad Core Algorithm	Complete introduction to multicalibration with MCGrad

What You'll Learn

01. MCGrad Core Algorithm

This comprehensive tutorial covers:

1. Why Multicalibration Matters - Understand the limitations of global calibration and why segment-level calibration is important
2. MCGrad Basics - Learn how to use the fit() and predict() API
3. Measuring Multicalibration - Use the Multicalibration Error (MCE) metric to evaluate calibration quality
4. Visualization - Plot global and segment-level calibration curves
5. Advanced Features - Explore feature importance, model serialization, numerical features, and custom hyperparameters

Running Tutorials

Option 1: Google Colab (Recommended)

Click the "Open in Colab" badge above to run tutorials directly in your browser. No local setup required!

Option 2: Local Jupyter

1. Install MCGrad with tutorial dependencies:

pip install "MCGrad[tutorials] @ git+https://github.com/facebookincubator/MCGrad.git"

2. Clone the repository and navigate to tutorials:

git clone https://github.com/facebookincubator/MCGrad.git
cd MCGrad/tutorials
jupyter notebook 01_mcgrad_core.ipynb

Option 3: VS Code

Open the .ipynb files directly in VS Code with the built-in Jupyter extension.

Contributing Tutorials

We welcome contributions! If you'd like to add a new tutorial:

1. Create a new .ipynb file in the tutorials/ directory
2. Follow the naming convention: XX_descriptive_name.ipynb
3. Include a Colab setup cell at the top (see existing tutorials for the pattern)
4. Add the tutorial to this documentation page
5. Submit a pull request

See the Contributing Guide for more details.