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Trees for Photo-Z » History » Version 1

Edward Kim, 01/10/2014 11:46 AM

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h1. Trees for Photo-Z
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TPZ [1] is a supervised machine learning, parallel algorithm that uses prediction
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trees and random forest techniques to produce both robust photometric redshift
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PDFs and ancillary information for a galaxy sample. A prediction tree is built
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by asking a sequence of questions that recursively split the input data taken
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from the spectroscopic sample, frequently into two branches, until a terminal
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leaf is created that meets a stopping criterion (e.g., a minimum leaf size or
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a variance threshold). The dimension in which the data is divided is chosen
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to be the one with highest information gain among the random subsample of
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dimensions obtained at every point. This process produces less correlated trees
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and allows to explore several configurations within the data. The small region
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bounding the data in the terminal leaf node represents a specific subsample of
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the entire data with similar properties. Within this leaf, a model is applied that
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provides a fairly comprehensible prediction, especially in situations where many
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variables may exist that interact in a nonlinear manner as is often the case with
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photo-z estimation.