Visibility: public Uploaded 05-12-2016 by Laurence Keijzer Weka_3.8.0 0 runs
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Weka implementation of IterativeClassifierOptimizer


Wweka.AdditiveRegression_DecisionStump(5)Full name of base classifier. (default: weka.classifiers.meta.LogitBoost)


-do-not-check-capabilitiesIf set, classifier capabilities are not checked before classifier is built (use with caution).
AIf set, average estimate is used rather than one estimate from pooled predictions.
EThe number of threads to use, which should be >= size of thread pool. (default 1)default: 1
FNumber of folds for cross-validation. (default 10)default: 10
HShrinkage parameter. (default 1)
IStep size for the evaluation, if evaluation is time consuming. (default 1)default: 1
LThe number of iterations to look ahead for to find a better optimum. (default 50)default: 50
OThe size of the thread pool, for example, the number of cores in the CPU. (default 1)
PThe size of the thread pool, for example, the number of cores in the CPU. (default 1)default: 1
QUse resampling instead of reweighting for boosting.
RNumber of runs for cross-validation. (default 1)default: 1
SRandom number seed. (default 1)default: 1
WFull name of base classifier. (default: weka.classifiers.meta.LogitBoost)default: weka.classifiers.meta.AdditiveRegression
ZZ max threshold for responses. (default 3)
batch-sizeThe desired batch size for batch prediction (default 100).
class-value-indexClass value index to optimise. Ignored for all but information-retrieval type metrics (such as roc area). If unspecified (or a negative value is supplied), and an information-retrieval metric is specified, then the class-weighted average metric used. (default -1)
metricEvaluation metric to optimise (default rmse). Available metrics: correct,incorrect,kappa,total cost,average cost,kb relative,kb information, correlation,complexity 0,complexity scheme,complexity improvement, mae,rmse,rae,rrse,coverage,region size,tp rate,fp rate,precision,recall, f-measure,mcc,roc area,prc areadefault: RMSE
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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