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

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

Visibility: public Uploaded 11-10-2019 by Jan van Rijn Weka_3.4.3 54 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"]
resumeSet whether classifier can continue training after performing therequested number of iterations. Note that setting this to true will retain certain data structures which can increase the size of the model.default: ["false"]
use-estimated-priorsUse estimated priors rather than uniform ones.default: ["false"]

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