Examples and Tutorials

This section provides comprehensive examples and tutorials for using Calibre.

Interactive Jupyter Notebooks

We provide focused, executable Jupyter notebooks for hands-on learning:

Notebook Overview

📚 Getting Started (Getting Started with Calibre)
  • Basic calibration workflow with realistic ML predictions

  • Choosing the right calibrator for your data

  • Visual validation with reliability diagrams

  • Quick start guide for new users

🔍 Validation and Evaluation (Validation and Evaluation)
  • Comprehensive calibration quality assessment

  • Mathematical property validation (bounds, monotonicity, granularity)

  • Performance across different miscalibration patterns

  • Edge case testing and robustness analysis

🩺 Diagnostics and Troubleshooting (Plateau Diagnostics Demo)
  • Plateau diagnostic tools for isotonic regression

  • Distinguishing genuine vs. limited-data flattening

  • Bootstrap stability analysis and progressive sampling

  • Decision framework for method selection

Performance Comparison (Performance Comparison)
  • Systematic comparison across all calibration methods

  • Performance on overconfident, underconfident, and distorted predictions

  • Computational efficiency and method ranking

  • Guidelines for choosing the optimal method

Running the Notebooks

To run these notebooks locally:

git clone https://github.com/finite-sample/calibre.git
cd calibre
pip install -e ".[dev]"
jupyter notebook docs/source/notebooks/

Or install required dependencies:

pip install calibre[examples]  # Installs matplotlib, seaborn, pandas

Additional Documentation Examples

Basic Usage Examples

The Basic Usage Examples section covers:

  • Simple calibration workflows

  • Choosing the right calibration method

  • Evaluating calibration quality

  • Common use cases and patterns

Advanced Usage Examples

The Advanced Usage Examples section includes:

  • Multi-class calibration strategies

  • Handling imbalanced datasets

  • Cross-validation for calibration

  • Custom calibration pipelines

Performance Benchmarks

The Performance Benchmarks section provides:

  • Comparative analysis of different methods

  • Performance on various dataset types

  • Computational efficiency comparisons