calculateRankSampling: Rank Sampling
In matpato/EEFR: Enhanced Ensemble Feature Ranking (EEFR)
Description
Usage
Arguments
Value
Description
Calculates Feature Ranking List sorted by its relevance
Usage
1
calculateRankSampling(weights, featuresNames, weightPerGroup, cutOff = -1)
Arguments
weights
of features list for each metric function for each dataset sample
featuresNames
to be ranked
weightPerGroup
of eachrank
cutOff:
0 (all features), k (k most ranked features), -1 (automatic k calculation)
Value
rank subset of features ordered by its relevance
matpato/EEFR documentation built on Dec. 21, 2021, 2:53 p.m.
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Description Usage Arguments Value
Description
Calculates Feature Ranking List sorted by its relevance
Usage
1 | calculateRankSampling(weights, featuresNames, weightPerGroup, cutOff = -1)
|
Arguments
weights |
of features list for each metric function for each dataset sample |
featuresNames |
to be ranked |
weightPerGroup |
of eachrank |
cutOff: |
0 (all features), k (k most ranked features), -1 (automatic k calculation) |
Value
rank subset of features ordered by its relevance
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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