ConformalPrediction.jl is a package for Predictive Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in MLJ (Blaom et al. 2020). Conformal Prediction is easy-to-understand, easy-to-use and model-agnostic and it works under minimal distributional assumptions. Intuitively, CP works under the premise of turning heuristic notions of uncertainty into rigorous uncertainty estimates through repeated sampling or the use of dedicated calibration data.
Features
- Examples available
- Documentation available
- Package for Predictive Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia
- Licensed under the MIT License
- Intuitively, CP works under the premise of turning heuristic notions of uncertainty into rigorous uncertainty estimates
- Repeated sampling and dedicated calibration data
Project Samples
Categories
Data VisualizationLicense
MIT LicenseFollow ConformalPrediction.jl
You Might Also Like
MongoDB Atlas runs apps anywhere
MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of ConformalPrediction.jl!