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weka.MetaCost_RandomForest

weka.MetaCost_RandomForest

Visibility: public Uploaded 21-04-2017 by Irma van den Brandt Weka_3.8.1 0 runs
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Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999.

Components

Wweka.RandomForest(12)Full name of base classifier. (default: weka.classifiers.rules.ZeroR)

Parameters

-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
CFile name of a cost matrix to use. If this is not supplied, a cost matrix will be loaded on demand. The name of the on-demand file is the relation name of the training data plus ".cost", and the path to the on-demand file is specified with the -N option.
INumber of bagging iterations. (default 10)default: 10
NName of a directory to search for cost files when loading costs on demand (default current directory).default: /Users/IrmavandenBrandt
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.rules.ZeroR)default: weka.classifiers.trees.RandomForest
batch-sizeThe desired batch size for batch prediction (default 100).
cost-matrixThe cost matrix in Matlab single line format.
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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