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In the one dimensional space of the feature of interest, data of different groups are distributed in different segments, and the segments may overlap. The N and S denote the number of data in the overlap between segments and the size of the overlap, respectively. Features are evaluated individually, and those with small N and S will be highly scored.
Hello I tried classification task using example train.dat posted on your github.
I have 2 questions about space_001d.name and space_002d.name
Thank you
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