mlrCPO (version 0.3.7-7)

randomForestSRC_filters: 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()