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User Guide

This guide covers everything you need to know to use nonconform effectively, from the underlying theory to production deployment.

Getting Started

If you're new to conformal prediction, start here:

Page Description
Statistical Concepts Quick reference for key statistical terms (p-values, FDR, exchangeability)
Conformal Inference Deep dive into how conformal prediction works and why it provides guarantees

Core Concepts

Page Description
Conformalization Strategies Split, Cross-Validation, Jackknife+, and Bootstrap strategies explained
Choosing Strategies Decision framework: which strategy to use for your dataset and requirements
Detector Compatibility How to use PyOD, scikit-learn, or your own custom detectors

Advanced Topics

Page Description
FDR Control Control false discovery rates when testing many observations
Weighted Conformal Handle distribution shift between training and test data

Practical Usage

Page Description
Input Validation Parameter constraints and what error messages mean
Batch Evaluation Evaluate performance on labeled test sets
Streaming Evaluation Online evaluation for real-time detection
Exchangeability Martingales Sequential evidence monitoring on streaming conformal p-values
Best Practices Production patterns, data preparation, and model selection
Logging Configure progress bars and debug output
Troubleshooting Solutions to common issues
  1. New to conformal prediction? Start with Statistical Concepts, then Conformal Inference
  2. Choosing a strategy? Read Choosing Strategies
  3. Going to production? Review Best Practices and Troubleshooting
  4. Dealing with distribution shift? Study Weighted Conformal