EvaluateNodeCandidateUsingRpart: Evaluate a feature using classification tree models
In thecomeonman/CURD: Implementation of the CURD algorithm for clustering
Description
Usage
Arguments
Value
View source: R/EvaluateNodeCandidateUsingRpart.R
Description
Classifier performance is measured as the average class error on OOB sample
This is done using 3-fold cross-validation, which is roughly the equivalent
of the OOB performance measured in RF
Usage
1
2
EvaluateNodeCandidateUsingRpart(matLabeledDataForClassifier,
vPredictorFeaturesForClassifier, cPredictedFeature)
Arguments
matLabeledDataForClassifier
todo
vPredictorFeaturesForClassifier
todo
cPredictedFeature
todo
Value
todo
thecomeonman/CURD documentation built on May 20, 2019, 7:37 a.m.
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Description Usage Arguments Value
View source: R/EvaluateNodeCandidateUsingRpart.R
Description
Classifier performance is measured as the average class error on OOB sample This is done using 3-fold cross-validation, which is roughly the equivalent of the OOB performance measured in RF
Usage
1 2 | EvaluateNodeCandidateUsingRpart(matLabeledDataForClassifier,
vPredictorFeaturesForClassifier, cPredictedFeature)
|
Arguments
matLabeledDataForClassifier |
todo |
vPredictorFeaturesForClassifier |
todo |
cPredictedFeature |
todo |
Value
todo
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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