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m_replaceMissing
weka.filters.unsupervised.attribute.ReplaceMissingValues m_replaceMissing
Filter to replace missing values
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m_nominalToBinary
weka.filters.supervised.attribute.NominalToBinary m_nominalToBinary
Filter to replace nominal attributes
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m_tree
weka.classifiers.trees.lmt.LMTNode m_tree
root of the logistic model tree
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m_fastRegression
boolean m_fastRegression
use heuristic that determines the number of LogitBoost iterations only once
in the beginning?
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m_convertNominal
boolean m_convertNominal
convert nominal attributes to binary ?
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m_splitOnResiduals
boolean m_splitOnResiduals
split on residuals?
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m_errorOnProbabilities
boolean m_errorOnProbabilities
use error on probabilties instead of misclassification for stopping
criterion of LogitBoost?
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m_minNumInstances
int m_minNumInstances
minimum number of instances at which a node is considered for splitting
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m_numBoostingIterations
int m_numBoostingIterations
if non-zero, use fixed number of iterations for LogitBoost
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m_weightTrimBeta
double m_weightTrimBeta
Threshold for trimming weights. Instances with a weight lower than this (as
a percentage of total weights) are not included in the regression fit.
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m_useAIC
boolean m_useAIC
If true, the AIC is used to choose the best LogitBoost iteration
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m_doNotMakeSplitPointActualValue
boolean m_doNotMakeSplitPointActualValue
Do not relocate split point to actual data value