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weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)

weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree)

Visibility: public Uploaded 16-08-2018 by Jason Weka_3.4.3 0 runs
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.

Parameters

-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).default: ["false"]
EThe number of threads to use for batch prediction, which should be >= size of thread pool. (default 1)default: ["1"]
HShrinkage parameter. (default 1)default: ["1.0"]
INumber of iterations. (current value 10)default: ["10"]
LThreshold on the improvement of the likelihood. (default -Double.MAX_VALUE)default: ["-1.7976931348623157E308"]
OThe size of the thread pool, for example, the number of cores in the CPU. (default 1)default: ["1"]
PPercentage of weight mass to base training on. (default 100, reduce to around 90 speed up)default: ["100"]
QUse resampling instead of reweighting for boosting.default: ["false"]
SRandom number seed. (default 1)default: ["1"]
WFull name of base classifier. (default: weka.classifiers.trees.DecisionStump)default: ["weka.classifiers.trees.REPTree"]
ZZ max threshold for responses. (default 3)default: ["3.0"]
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: []
output-debug-infoIf set, classifier is run in debug mode and may output additional info to the consoledefault: ["false"]
use-estimated-priorsUse estimated priors rather than uniform ones.default: ["false"]

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