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weka.MultiClassClassifier_AdaBoostM1_RandomForest

weka.MultiClassClassifier_AdaBoostM1_RandomForest

Visibility: public Uploaded 21-04-2017 by Zeno van Cauter Weka_3.8.1 1 runs
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  • Verified_Supervised_Classification weka weka_3.8.1
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Weka implementation of MultiClassClassifier

Components

Wweka.AdaBoostM1_RandomForest(6)Full name of base classifier. (default: weka.classifiers.functions.Logistic)

Parameters

-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
CUse conjugate gradient descent rather than BFGS updates.
LUse log loss decoding for random and exhaustive codes
MSets the method to use. Valid values are 0 (1-against-all), 1 (random codes), 2 (exhaustive code), and 3 (1-against-1). (default 0)default: 0
PUse pairwise coupling (only has an effect for 1-against1)
RSets the multiplier when using random codes. (default 2.0)default: 2.0
SRandom number seed. (default 1)default: 1
WFull name of base classifier. (default: weka.classifiers.functions.Logistic)default: weka.classifiers.meta.AdaBoostM1
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).
output-debug-infoIf set, classifier is run in debug mode and may output additional info to the console

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