Visibility: public Uploaded 04-04-2014 by Jan van Rijn Moa_2014.03 270 runs
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  • study_11 Verified_Supervised_Data_Stream_Classification
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A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. Hoeffding trees exploit the fact that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound, which quantifies the number of observations (in our case, examples) needed to estimate some statistics within a prescribed precision (in our case, the goodness of an attribute).


bbinarySplits: Only allow binary splits.default: false
csplitConfidence: The allowable error in split decision, values closer to 0 will take longer to decide.default: 1.0E-7
dnominalEstimator: Nominal estimator to use.default: NominalAttributeClassObserver
ememoryEstimatePeriod: How many instances between memory consumption checks.default: 1000000
ggracePeriod: The number of instances a leaf should observe between split attempts.default: 200
lleafprediction: Leaf prediction to use.default: NBAdaptive
mmaxByteSize: Maximum memory consumed by the tree.default: 33554432
nnumericEstimator: Numeric estimator to use.default: GaussianNumericAttributeClassObserver
pnoPrePrune: Disable pre-pruning.default: false
qnbThreshold: The number of instances a leaf should observe before permitting Naive Bayes.default: 0
rremovePoorAtts: Disable poor attributes.default: false
ssplitCriterion: Split criterion to use.default: InfoGainSplitCriterion
ttieThreshold: Threshold below which a split will be forced to break ties.default: 0.05
zstopMemManagement: Stop growing as soon as memory limit is hit.default: false


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