Visibility: public Uploaded 29-03-2017 by Derek Lagrouw Weka_3.8.1 2 runs
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  • Verified_Supervised_Classification weka weka_3.8.1
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.


Wweka.MultilayerPerceptron(8)Full name of base classifier. (default: weka.classifiers.trees.REPTree)


-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
INumber of iterations. (current value 10)default: 10
LMaximum tree depth (default -1, no maximum)
MSet minimum number of instances per leaf (default 2).
NNumber of folds for reduced error pruning (default 3).
OCalculate the out of bag error.
PSize of each bag, as a percentage of the training set size. (default 100)default: 100
RSpread initial count over all class values (i.e. don't use 1 per value)
SRandom number seed. (default 1)default: 1
VSet minimum numeric class variance proportion of train variance for split (default 1e-3).
WFull name of base classifier. (default: weka.classifiers.trees.REPTree)default: weka.classifiers.functions.MultilayerPerceptron
batch-sizeThe desired batch size for batch prediction (default 100).
num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).
num-slotsNumber of execution slots. (default 1 - i.e. no parallelism) (use 0 to auto-detect number of cores)default: 1
output-debug-infoIf set, classifier is run in debug mode and may output additional info to the console
output-out-of-bag-complexity-statisticsWhether to output complexity-based statistics when out-of-bag evaluation is performed.
printPrint the individual classifiers in the output
represent-copies-using-weightsRepresent copies of instances using weights rather than explicitly.
store-out-of-bag-predictionsWhether to store out of bag predictions in internal evaluation object.


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