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weka.MultiClassClassifierUpdateable_SGD

weka.MultiClassClassifierUpdateable_SGD

Visibility: public Uploaded 05-12-2016 by Chris Jansen Weka_3.8.0 1 runs
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  • Verified_Supervised_Classification weka weka_3.8.0
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Weka implementation of MultiClassClassifierUpdateable

Components

Wweka.SGD(5)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).
CThe epsilon threshold (epsilon-insenstive and Huber loss only, default = 1e-3)
EThe number of epochs to perform (batch learning only, default = 500)
FSet the loss function to minimize. 0 = hinge loss (SVM), 1 = log loss (logistic regression), 2 = squared loss (regression), 3 = epsilon insensitive loss (regression), 4 = Huber loss (regression). (default = 0)
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
NDon't normalize the data
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.functions.SGD
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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