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Uploaded 20-04-2018 by
Tim Beurskens
RapidMiner_8.1.001
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Parameters
SplitData__local_random_seed Specifies the local random seed default: 1992 SplitData__partitions The partitions that should be created. SplitData__sampling_type Defines the sampling type of this operator. default: automatic SplitData__use_local_random_seed Indicates if a local random seed should be used. default: false k-NN__divergence Select divergence default: GeneralizedIDivergence k-NN__k The used number of nearest neighbors. default: 1 k-NN__kernel_a The kernel parameter a. default: 1.0 k-NN__kernel_b The kernel parameter b. default: 0.0 k-NN__kernel_degree The kernel parameter degree. default: 3.0 k-NN__kernel_gamma The kernel parameter gamma. default: 1.0 k-NN__kernel_shift The kernel parameter shift. default: 1.0 k-NN__kernel_sigma1 The kernel parameter sigma1. default: 1.0 k-NN__kernel_sigma2 The kernel parameter sigma2. default: 0.0 k-NN__kernel_sigma3 The kernel parameter sigma3. default: 2.0 k-NN__kernel_type The kernel type default: radial k-NN__measure_types The measure type default: MixedMeasures k-NN__mixed_measure Select measure default: MixedEuclideanDistance k-NN__nominal_measure Select measure default: NominalDistance k-NN__numerical_measure Select measure default: EuclideanDistance k-NN__weighted_vote Indicates if the votes should be weighted by similarity. default: false
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Parameter:
none
SplitData local random seed
SplitData partitions
SplitData sampling type
SplitData use local random seed
k-NN divergence
k-NN k
k-NN kernel a
k-NN kernel b
k-NN kernel degree
k-NN kernel gamma
k-NN kernel shift
k-NN kernel sigma1
k-NN kernel sigma2
k-NN kernel sigma3
k-NN kernel type
k-NN measure types
k-NN mixed measure
k-NN nominal measure
k-NN numerical measure
k-NN weighted vote
Supervised Classification
Supervised Regression
Learning Curve
Supervised Data Stream Classification
Clustering
Machine Learning Challenge
Survival Analysis
Subgroup Discovery
area under roc curve
average cost
binominal test
build cpu time
build memory
c index
chi-squared
class complexity
class complexity gain
confusion matrix
correlation coefficient
cortana quality
coverage
f measure
information gain
jaccard
kappa
kb relative information score
kohavi wolpert bias squared
kohavi wolpert error
kohavi wolpert sigma squared
kohavi wolpert variance
kononenko bratko information score
matthews correlation coefficient
mean absolute error
mean class complexity
mean class complexity gain
mean f measure
mean kononenko bratko information score
mean precision
mean prior absolute error
mean prior class complexity
mean recall
mean weighted area under roc curve
mean weighted f measure
mean weighted precision
weighted recall
number of instances
os information
positives
precision
predictive accuracy
prior class complexity
prior entropy
probability
quality
ram hours
recall
relative absolute error
root mean prior squared error
root mean squared error
root relative squared error
run cpu time
run memory
run virtual memory
scimark benchmark
single point area under roc curve
total cost
unclassified instance count
usercpu time millis
usercpu time millis testing
usercpu time millis training
webb bias
webb error
webb variance
joint entropy
pattern team auroc10
wall clock time millis
wall clock time millis training
wall clock time millis testing
unweighted recall