4833 1 moa.AccuracyWeightedEnsemble_HoeffdingTree 1 Moa__16.04_April_2016_1.0 Moa implementation of AccuracyWeightedEnsemble 2016-10-26T10:47:39 English Moa__16.04_April_2016 c option 500 chunkSize: The chunk size used for classifier creation and evaluation. f option 10 numFolds: Number of cross-validation folds for candidate classifier testing. l baselearner trees.HoeffdingTree -l NB -e 1000 -g 100 -c 0.01 learner: Classifier to train. n option 15.0 memberCount: The maximum number of classifier in an ensemble. r option 30.0 storedCount: The maximum number of classifiers to store and choose from when creating an ensemble. l 4811 1 moa.HoeffdingTree 7 Moa__16.04_April_2016_1.0 Moa implementation of HoeffdingTree 2016-10-05T19:35:32 English Moa__16.04_April_2016 b flag false binarySplits: Only allow binary splits. c option 1.0E-7 splitConfidence: The allowable error in split decision, values closer to 0 will take longer to decide. d baselearner NominalAttributeClassObserver nominalEstimator: Nominal estimator to use. e option 1000000 memoryEstimatePeriod: How many instances between memory consumption checks. g option 200 gracePeriod: The number of instances a leaf should observe between split attempts. l option NBAdaptive leafprediction: Leaf prediction to use. m option 33554432 maxByteSize: Maximum memory consumed by the tree. n baselearner GaussianNumericAttributeClassObserver numericEstimator: Numeric estimator to use. p flag false noPrePrune: Disable pre-pruning. q option 0 nbThreshold: The number of instances a leaf should observe before permitting Naive Bayes. r flag false removePoorAtts: Disable poor attributes. s baselearner InfoGainSplitCriterion splitCriterion: Split criterion to use. t option 0.05 tieThreshold: Threshold below which a split will be forced to break ties. z flag false stopMemManagement: Stop growing as soon as memory limit is hit. Verified_Supervised_Data_Stream_Classification Verified_Supervised_Data_Stream_Classification