| randomForestSRC_filters {mlrCPO} | R Documentation |
Filter “randomForestSRC_importance” computes the importance of random forests fitted in package randomForestSRC. The concrete method is selected via the 'method' parameter. Possible values are 'permute' (default), 'random', 'anti', 'permute.ensemble', 'random.ensemble', 'anti.ensemble'. See the VIMP section in the docs for [randomForestSRC::rfsrc] for details.
Description
Filter “randomForestSRC_importance” computes the importance of random forests fitted in package randomForestSRC. The concrete method is selected via the 'method' parameter. Possible values are 'permute' (default), 'random', 'anti', 'permute.ensemble', 'random.ensemble', 'anti.ensemble'. See the VIMP section in the docs for [randomForestSRC::rfsrc] for details.
Filter “randomForestSRC_var.select” uses the minimal depth variable selection proposed by Ishwaran et al. (2010) ('method = "md"') or a variable hunting approach ('method = "vh"' or 'method = "vh.vimp"'). The minimal depth measure is the default.
See Also
Other filter:
cpoFilterAnova(),
cpoFilterCarscore(),
cpoFilterChiSquared(),
cpoFilterFeatures(),
cpoFilterGainRatio(),
cpoFilterInformationGain(),
cpoFilterKruskal(),
cpoFilterLinearCorrelation(),
cpoFilterMrmr(),
cpoFilterOneR(),
cpoFilterPermutationImportance(),
cpoFilterRankCorrelation(),
cpoFilterRelief(),
cpoFilterRfCImportance(),
cpoFilterRfImportance(),
cpoFilterRfSRCImportance(),
cpoFilterRfSRCMinDepth(),
cpoFilterSymmetricalUncertainty(),
cpoFilterUnivariate(),
cpoFilterVariance()
Other filter:
cpoFilterAnova(),
cpoFilterCarscore(),
cpoFilterChiSquared(),
cpoFilterFeatures(),
cpoFilterGainRatio(),
cpoFilterInformationGain(),
cpoFilterKruskal(),
cpoFilterLinearCorrelation(),
cpoFilterMrmr(),
cpoFilterOneR(),
cpoFilterPermutationImportance(),
cpoFilterRankCorrelation(),
cpoFilterRelief(),
cpoFilterRfCImportance(),
cpoFilterRfImportance(),
cpoFilterRfSRCImportance(),
cpoFilterRfSRCMinDepth(),
cpoFilterSymmetricalUncertainty(),
cpoFilterUnivariate(),
cpoFilterVariance()