MAPIE

Getting Started

Quick Start with MAPIE
The conformalization (or “calibration”) set
- 1. Split conformal predictions
  - Split conformal predictions with a pre-trained model
  - Split conformal predictions with an untrained model
- 2. Cross conformal predictions
Choosing the right algorithm
MAPIE v1 release notes

Measure predictions uncertainty

Prediction intervals (regression)
Prediction sets (classification)

Control prediction errors

Getting started with risk control in MAPIE
Use MAPIE to control the precision of a binary classifier
All risk control examples
- 1. Quickstart examples
- 2. Advanced analysis
  - 1. Quickstart examples
    - Use MAPIE to control the precision of a binary classifier
  - 2. Advanced analysis
Additional resources: external risk control package

Calibrate multi-class predictions

Theoretical Description
- Top-Label
- References
Calibration examples
- 1. Quickstart examples
- 2. Advanced analysis
  - 1. Quickstart examples
    - Testing for calibration in binary classification settings
  - 2. Advanced analysis
    - Evaluating the asymptotic convergence of p-values
Calibration notebooks
- 1. Top-label calibration for outputs of ML models : notebook

Question & Answers

Metrics: how to measure conformal prediction performance?
Mondrian: how to use prior knowledge on groups when measuring uncertainty?
- Tutorial: how to ensure fairness across groups with Mondrian
- Theoretical Description
  - References
How to control LLM risks?

API

MAPIE API

MAPIE

Prediction sets (classification)
All classification examples
4. Other notebooks
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4. Other notebooks

This section lists a series of Jupyter notebooks hosted on the MAPIE Github repository that can be run on Google Colab:

Deep learning with MAPIE on the Cifar10 dataset

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