gchers/random-world
Standalone implementation of Conformal Prediction and other distribution-free Machine Learning methods.
This project helps data scientists, machine learning engineers, and researchers assess the reliability of their machine learning predictions. It takes in structured data, typically in CSV files, that includes features and labels. The output provides confidence levels (p-values) or explicit predictions, helping users understand when their model's predictions might be less certain.
No commits in the last 6 months.
Use this if you need to quantify the confidence or uncertainty around individual predictions from your machine learning models, especially when dealing with critical applications or when data might deviate from expected patterns.
Not ideal if you are looking for a general-purpose machine learning library to build models from scratch, as its primary focus is on prediction confidence and exchangeability testing, not model training.
Stars
14
Forks
4
Language
Rust
License
MIT
Category
Last pushed
Aug 23, 2019
Monthly downloads
80
Commits (30d)
0
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