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weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic))))

weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic))))

Visibility: public Uploaded 19-07-2024 by Jan van Rijn Weka_3.9.6 4 runs
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

Parameters

-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).default: ["false"]
INumber of iterations. (current value 10)default: ["10"]
OCalculate the out of bag error.default: ["false"]
PSize of each bag, as a percentage of the training set size. (default 100)default: ["100"]
SRandom number seed. (default 1)default: ["1"]
WFull name of base classifier. (default: weka.classifiers.trees.REPTree)default: ["weka.classifiers.meta.Bagging"]
batch-sizeThe desired batch size for batch prediction (default 100).default: []
num-decimal-placesThe number of decimal places for the output of numbers in the model (default 2).default: []
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 consoledefault: ["false"]
output-out-of-bag-complexity-statisticsWhether to output complexity-based statistics when out-of-bag evaluation is performed.default: ["false"]
printPrint the individual classifiers in the outputdefault: ["false"]
represent-copies-using-weightsRepresent copies of instances using weights rather than explicitly.default: ["false"]
store-out-of-bag-predictionsWhether to store out of bag predictions in internal evaluation object.default: ["false"]

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