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[ascl:1903.009] PRF: Probabilistic Random Forest

PRF (Probabilistic Random Forest) is a machine learning algorithm for noisy datasets. The PRF is a modification of the long-established Random Forest (RF) algorithm, and takes into account uncertainties in the measurements (i.e., features) as well as in the assigned classes (i.e., labels). To do so, the Probabilistic Random Forest (PRF) algorithm treats the features and labels as probability distribution functions, rather than as deterministic quantities.

Code site:
https://github.com/ireis/PRF
Described in:
http://adsabs.harvard.edu/abs/2019AJ....157...16R
Bibcode:
2019ascl.soft03009R

Views: 125

ascl:1903.009
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