Welcome¶
About
MLZ, “Machine Learning and photo-Z” is a parallel python framework that computes fast and robust photometric redshift PDFs using Machine Learning algorithms. In particular, it uses a supervised technique with prediction trees and random forest through TPZ or a unsupervised methods with self organizing maps and random atlas through SOMz. It can be easily extended to other regression or classification problems.
References¶
These are the references related to this framework where detailed information about these methods can be found.
- Carrasco Kind, M., & Brunner, R. J., 2013 “TPZ : Photometric redshift PDFs and ancillary information by using prediction trees and random forests”, MNRAS, 432, 1483 (Link)
- Carrasco Kind, M., & Brunner, R. J., 2014, “SOMz : photometric redshift PDFs with self organizing maps and random atlas” , MNRAS, in press. (Link)
- Carrasco Kind, M., & Brunner, R. J., 2013, “Implementing Probabilistic Photometric Redshifts”, in Astronomical Society of the Pacific Conference Series, Vol. 475, Astronomical Data Analysis Software and Systems XXII (ADASSXXII), Freidel D., ed., p. 69 (Link)
Contents¶
This is a brief documentation of MLZ and the routines included