PRObabilistic CLAssification Metrics
This is a space for developing the metrics for the Photometric LSST AStronomical TIme Series Classification Challenge (PLAsTiCC).
An immediate goal is to implement the metrics described here and demonstrate them on mock classification results, in an effort to identify the vulnerabilities and strengths of the metrics we're considering.
This repository will serve not only as a space for experimenting with how to combine metrics to declare a winner for the Kaggle/RAMP competition requirements but also for testing further metrics for science-specific papers. The code thus has a flexible, modular architecture that can be recycled for future competitions with different science goals.
To get involved, check out the READMEs in the code directories.
- Alex Malz (NYU)
- Anita Bahmanyar (U. Toronto)
- Rahul Biswas (U. Stockholm)
- [Rafael Martinez-Galarza]((https://github.com/juramaga) (CfA)
- Renee Hlozek (U. Toronto)
- Gautham Narayan (STScI)
- The PLAsTiCC Team