Flow
rm.process(split_data,k_nn)

rm.process(split_data,k_nn)

Visibility: public Uploaded 20-04-2018 by Tim Beurskens RapidMiner_8.1.001 0 runs
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A RapidMiner Flow

Parameters

SplitData__local_random_seedSpecifies the local random seeddefault: 1992
SplitData__partitionsThe partitions that should be created.
SplitData__sampling_typeDefines the sampling type of this operator.default: automatic
SplitData__use_local_random_seedIndicates if a local random seed should be used.default: false
k-NN__divergenceSelect divergencedefault: GeneralizedIDivergence
k-NN__kThe used number of nearest neighbors.default: 1
k-NN__kernel_aThe kernel parameter a.default: 1.0
k-NN__kernel_bThe kernel parameter b.default: 0.0
k-NN__kernel_degreeThe kernel parameter degree.default: 3.0
k-NN__kernel_gammaThe kernel parameter gamma.default: 1.0
k-NN__kernel_shiftThe kernel parameter shift.default: 1.0
k-NN__kernel_sigma1The kernel parameter sigma1.default: 1.0
k-NN__kernel_sigma2The kernel parameter sigma2.default: 0.0
k-NN__kernel_sigma3The kernel parameter sigma3.default: 2.0
k-NN__kernel_typeThe kernel typedefault: radial
k-NN__measure_typesThe measure typedefault: MixedMeasures
k-NN__mixed_measureSelect measuredefault: MixedEuclideanDistance
k-NN__nominal_measureSelect measuredefault: NominalDistance
k-NN__numerical_measureSelect measuredefault: EuclideanDistance
k-NN__weighted_voteIndicates if the votes should be weighted by similarity.default: false

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