randomForestSRC_filters: Filter "randomForestSRC_importance" computes the importance...

In mlrCPO: Composable Preprocessing Operators and Pipelines for Machine Learning

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.

See Also

Other filter: cpoFilterAnova(), cpoFilterCarscore(), cpoFilterChiSquared(), cpoFilterFeatures(), cpoFilterGainRatio(), cpoFilterInformationGain(), cpoFilterKruskal(), cpoFilterLinearCorrelation(), cpoFilterMrmr(), cpoFilterOneR(), cpoFilterPermutationImportance(), cpoFilterRankCorrelation(), cpoFilterRelief(), cpoFilterRfCImportance(), cpoFilterRfImportance(), cpoFilterRfSRCImportance(), cpoFilterSymmetricalUncertainty(), cpoFilterUnivariate(), cpoFilterVariance()

mlrCPO documentation built on June 17, 2025, 1:07 a.m.