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:
Interactive Examples
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