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[ascl:1403.003] MLZ: Machine Learning for photo-Z

The parallel Python framework MLZ (Machine Learning and photo-Z) computes fast and robust photometric redshift PDFs using Machine Learning algorithms. It uses a supervised technique with prediction trees and random forest through TPZ that can be used for a regression or a classification problem, or a unsupervised methods with self organizing maps and random atlas called SOMz. These machine learning implementations can be efficiently combined into a more powerful one resulting in robust and accurate probability distributions for photometric redshifts.

Code site:
http://matias-ck.com/mlz/ https://github.com/mgckind/MLZ
Described in:
http://adsabs.harvard.edu/abs/2014MNRAS.442.3380C
Bibcode:
2014ascl.soft03003C

Views: 1713

ascl:1403.003
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